In late September, a customer asked Meta’s Muse to buy something on Amazon and got a popup instead of a confirmation. The popup wasn’t addressed to Meta. It was addressed to the customer, telling them that letting an unauthorized AI agent act on their behalf violated the terms they agreed to when they opened the account.

Amazon apparently asked Meta to remove Amazon’s website from its agent, but Meta refused. Meta’s apparent workaround was to have Muse access Amazon without declaring itself as an agent. Muse also captures the customer’s Amazon login credentials and stores them without permission. Meta says Muse never sees passwords or payment details. Given Meta’s reputation, it’s hard to believe that they are being upfront about what Muse does. But from an Amazon perspective, the pattern is bigger than this one fight. Amazon has shut out shopping agents from Google, OpenAI, Perplexity and Anthropic over the past year. eBay barred third-party buying agents in February. Kohl’s has restricted them too.

The usual explanation is the $68 billion advertising business, which runs on people searching and scrolling on Amazon’s own pages and which an agent that shops elsewhere and arrives only to pay would hollow out. That seems the most logical and tactical response. Amazon also argues that an outside agent skips the personalization they spent two decades building, so you end up with recommendations that have nothing to do with you. Even that is self-serving, and still not wrong.

This wasn’t Amazon’s first rodeo. Amazon sued Perplexity over its Comet browser, won an order blocking it in March, and lost that order in August when the Ninth Circuit held that the federal anti-hacking law contemplates access by a person, that an AI assistant is a tool rather than a person, and that the customer is therefore the one accessing Amazon. The court declined to reconsider in September, and went out of its way to say it wasn’t writing new rules for agentic AI or touching anyone’s right to govern access through their own terms. So the question went back to private agreement and whatever a company can enforce technically, which lands it with whoever runs the experience layer.

Meanwhile, Shopify opened its merchants to Muse and wired in its own checkout; Walmart and Best Buy have been welcoming, and Google launched an open commerce standard in January with Shopify, Walmart, Target, Etsy and Wayfair. Shopify says AI-referred orders grew about thirteenfold year over year last quarter. Nobody in either camp is being visionary or reactionary. They’re reading their own revenue model, and a business that sells attention reads it differently than one that sells transactions.

Whichever way it lands, someone in CX has to implement it, and that popup is a brand moment delivered at the worst possible second, blaming the customer for a fight between two enormous companies.

But let’s focus on the substance of Amazon’s complaint. The agent wouldn’t say what it was, wouldn’t say who sent it, and may have been holding a customer’s credentials. Those are fair questions, if simply from a cybersecurity perspective.

IEEE 7012 (MyTerms) was written for this. It lets a person arrive with their own machine-readable terms, lets the business agree to them, and leaves a record both sides can point to later. Not a consent banner, which asks you to accept what someone else already decided, but an agreement between two parties. It answers Amazon’s questions directly. Who this is, who authorized it, and what it can do with what it learns.

The Ninth Circuit made that case for MyTerms without meaning to. If the customer is the one accessing it, the customer’s terms should govern the exchange. I’d like us to get there before more companies decide a wall is the only option.

A new hire in her second week used to spend the day on forty relatively easy calls. Order status, password resets, a return she could process while the customer waited for a reply. Within a few weeks, she knew the catalog, the three things that go wrong most often, and what a frustrated customer sounds like.

With the front end increasingly moving to AI-powered chat, those inbound calls are gone. Her first live conversation now is with a customer who has already been through two agent sessions, is on an edge-case contract, and whose frustration levels have already crossed a threshold.

I’ve watched people come out of that first job and end up running product lines twenty years later. The contact center was never designed as a school, which is part of why nobody is protecting it now. The curriculum was an accident of volume. Easy calls arrived in large numbers, hard ones arrived rarely, and a new person climbed that gradient without anyone planning it.

Very few systems around it recorded what was happening. Workforce management optimized staffing against volume. QA scorecards measured adherence on individual calls. The tiering model existed to keep expensive people away from cheap work. Learning was a side effect that no tool tracked and no budget line paid for, so it’s easy to automate away without anyone noticing it’s gone.

Agents take the volume first, because inbound volume is a game of large numbers. What’s left for people is work that needs judgment, context, and some tolerance for ambiguity. That’s a fair description of a senior role and a terrible description of an entry-level one. You can’t staff the top of a skill curve with people who never had a chance to climb it.

I think this will not be immediately visible, but it will have long term consequences. It arrives as a slow shortage of expertise, maybe even years from now, when the people who would have been your directors turn out never to have had the job that made them. But by then, connecting the dots from a successful AI chatbot deployment to management and judgement gap will be hard to make.

The hiring math changes as well. If the only human work left needs judgment on day one, you’re recruiting from a smaller and more expensive pool for every seat, and the internal pipeline that used to feed it is no longer producing. Most service organizations have built their cost models on the assumption that they can hire capable people at entry level and grow them. That assumption is shifting, and budgets haven’t caught up.

There’s a second-order version worth watching too. AI agents are learning from every exchange as well, and they’re learning faster than the average new hire did. A CX leader has to decide which of those two curves the company is actually investing in, because how both are treated will have long term implications.

The opening here is real, and it starts with something CX already owns. Every resolved case in the archive is a worked example. A new hire can sit with thousands of them, with an agent as a tutor explaining why this account got a credit and that one didn’t, which is a better education than shadowing whichever veteran happened to be free that week.

The wider point is that CX will hit this before the rest of the company does. Service automates earliest because the work seems repetitive and the volume is high. Finance, marketing, and legal are on the same path a few years behind. Whatever CX leaders work out about building expertise will become the template the rest of the business comes to ask for.

I don’t have an obvious answer for what replaces the queue, and it’s too early in the AI era to know. The opportunity is to recognize the long-term design refactoring that this process will launch. Recognizing the dynamics are being altered by the arrival of AI at the edge is the first step in surfacing visibility for the next era of customer experience.

Around 2 a.m., the compressor in a restaurant’s walk-in cooler starts drawing more power than it should. Its own agent notices the drift and checks the warranty. The owner’s agent already knows there’s a hundred-person private event on Saturday and that losing the cooler before then isn’t an option. Between them, they send a parts request to the distributor with a Friday deadline and the reason attached.

For once, the distributor knows why the part is needed and what happens if it’s late. The manufacturer, a step further back, learns that this compressor model is drifting in humid kitchens, months before a single warranty claim would have told it anything.

My recent conversations with companies that are reaching beyond the front end AI agent usually take the shape of the restaurant scene above. Nobody at the restaurant filed a ticket, and nobody at the distributor had to call and ask how urgent it was. The kind of information that usually gets lost, or gets relayed badly by three different people, moved with the request on its own. This isn’t an exclusive AI narrative, per se, but the right agentic AI deployments will reflect this scenario and begin to realign the entire value chain.

In The Edge of the Enterprise I argued that your own personal agent gives Customer Experience systems the earliest read on demand in the company. Once customers and products carry agents of their own, that kind of context will start to move through the rest of the supply chain too, and I think the consequences reach further than most CX conversations currently allow for.

Historically, each tier of the supply chain has seen only the order from the tier in front of it. The distributor sees a purchase order, the manufacturer sees the distributor’s replenishment, and the component supplier sees a forecast built on top of both. Every handoff loses some of the reasoning. Industries spent decades carrying safety stock, prepositioning parts, and deploying field service technicians against a plan with little context. I spent a long stretch of my career working with customers deploying service software, and I rarely saw a service record travel beyond the company that created it. The ticket lived in one system and the purchase order in another, and the supplier never saw either one.

Agents on both ends change what can make the trip. The customer’s agent brings intent and constraints, and the product’s agent reports its own condition and usage, which is far more reliable than anything pieced together from a service ticket. We’ve had connected products for more than a decade, and most of their telemetry ended up on dashboards nobody looked at after the first month. An agent, on the receiving end, can act on the reading. It can decide the drift matters, check the warranty, and ask for a part with the evidence attached. When that context moves with the request, the upstream parties get the reasoning they’ve always had to guess at.

Manufacturers have spent enormous sums trying to learn how their products behave in the field, and distributors have built whole planning practices around estimating urgency. In an AI agentic future, a good share of that information will show up with the request.

This changes where customer experience actually gets shaped. Whether the restaurant’s event goes well depends on a distributor and a manufacturer the owner has never spoken to, and on whether the context reached them intact. CX leaders who have focused on their own front line will increasingly find their customers’ experience being shaped two or three tiers away, by companies they have no direct relationship with.

None of this happens by default. The owner’s agent decides what gets shared and under what terms, and it has no obligation to tell a distributor anything. This is where protocols like MyTerms help establish enforceable contracts to share such critical data. In theory, going back to the EDI days, sharing critical information has been at the heart of modern supply chain management. Missing from that conversation has been the untapped CX data. It has been missing because this data was just outside of what could be captured, permission, and transmitted upstream. Agentic AI will significantly increase the impact that customers and products have on the entire supply chain. CX, in this world, is not an end point, rather a critical element to driving greater value across the entire workflow.

So trust starts to behave like a supply chain asset, one that determines how much any given company gets to know about the demand it serves. Being the largest supplier in the chain won’t guarantee much of it. The agents will share with whoever has earned it, and I suspect most supply chain leaders haven’t yet thought about what earning it looks like. I’d like to think CX leaders will be the ones who show them.

A customer tells your agent she needs the shipment before the 14th, because the office is moving that weekend and after that there’s nobody at the dock.

That conversation happened in your channel. It contains a date, a reason, and a fairly clear consequence, and none of it will reach anyone who plans supply. Historically, that was a reasonable outcome, since there was no practical way to get it there.

Voice of the customer work has always had a credibility problem with operations, largely due to it’s historical framing. You can bring scores, verbatims, and a well-run survey program, and you’ll get a respectful hearing and not much else. The reason isn’t politics. Themes arrive too late and too vague to move a decision that’s already been made on a spreadsheet, and sampling four hundred calls a quarter was built to tell you how customers feel about you, which is a different question from how much of anything to build.

What’s changing is the volume and the structure. Agents are handling conversations at the edge of the enterprise, spoken and written, all day, and material that used to sit in recordings nobody had time to listen to is becoming something you can actually query. This is where the hidden value resides, ready to be extracted. An agent picks up why a customer wants something, when they really need it, what they’d take instead, and how much patience is left in the account, because customers surface that kind of insight in natural conversation rather than in the data-entry fields of your legacy ERP system.

The systems that run supply were never going to get there. Order management picks up the decision after it’s made, forecasting works from the pattern those decisions form, and distribution tracks where things went. They describe outcomes, which is what they were built to do. The reasoning behind the outcome wasn’t recorded anywhere, because until recently there was nothing capable of recording it.

This changes what a CX organization is for, and I don’t think many CX leaders have recognized this strategic shift yet. The function has spent twenty years downstream, reporting on the results of decisions made somewhere else. The conversation layer sits upstream of the order, which puts the earliest usable read on demand in the company inside your systems.

The raw material has been captured by the enterprise for years. Contact centers have recorded calls and stored chat transcripts for coaching, dispute resolution, and whatever the regulators ask for, and that archive has mostly sat there, because reading it in bulk was never realistic. Agents can work through it continuously now, and what surfaces looks different from a QA report. A cluster of accounts pushing on the same delivery window. A region where nobody will accept a substitution anymore. A product line where the complaints have shifted from price to availability.

Those are supply signals that happen to arrive through a customer conversation. When they come out of CX with evidence attached, the character of the conversation with the rest of the company changes. Satisfaction reporting after the fact is a modest thing to bring to an operating review next to the earliest available view of what demand is about to do. Planning teams have never thought of service as a source of that, which was a fair assumption for most of the last twenty years. The evidence is what changes it, and it moves CX into the strategic conversation the C-suite is already having about supply.

The bigger shift is what CX becomes as agents move to the edge of the enterprise. Service has spent its whole history as the last function to learn about a problem. The conversation layer makes it the first to watch intent take shape, weeks before that intent turns into an order, an allocation, or a shortage somebody has to explain to the board. None of that is really a tooling question. It’s a question about what the function is for, and it’s worth answering before the rest of the company works out what you’ve been sitting on.

Almost every agentic CX demo has the same moment in it. The customer says something vague and human, the assistant understands it perfectly, and then it does something no web form has ever done. It offers to hold the item, or moves the delivery to Friday, or waives the fee because it can see this is the second time.

The room nods along and the demo ends right there. Which is convenient, because everything hard happens in the next sequence of steps.

That promise has to go somewhere. Holding the item means reserving inventory that somebody’s forecast already counted on. A Friday delivery commitment means a truck that is either in the right city that night or is not. The waived fee is margin, given away by a system that has never carried a number.

None of this used to be a problem, because promises moved slowly. Marketing made them to a broadcast audience, operations built a plan that came close, and service apologized for the difference.

Real time context changes the speed. An assistant that knows your last delivery ran late will make you a better promise than any pricing table could. It will also make a much more specific one, and specific is the part the rest of the company is not ready for.

Behind that assistant sits a system that decides whether a date can be promised. It was built to answer one question, once, when the order is placed, reviewing a picture of supply that refreshed overnight. Ask it whether Friday still works if the customer will split the shipment, and it struggles to respond, because it was never designed for that kind of variance.

So the assistant knows a great deal and the system behind it isn’t built around these new context parameters, especially when they arrive in real time. I have been calling that drop the context cliff, for lack of a better name. The assistant has the reason behind the request and the mood of the last three conversations. The order record takes a date, a quantity, and a note nobody reads. The rest falls off the edge, mostly because there is no field for it.

Which explains a failure people keep misreading. The failure almost always happens at the next critical decision step after the interface. Success in the engagement numbers does not always translate into success in the bigger picture. The context captured at the experience layer needs to enable dynamic decisions within operations.

When it does not, the customer reads the whole sequence as a company that seemed to understand them and then acted like it never had.

And the drop keeps getting steeper. Every dollar spent on the experience layer buys more context, and more context buys a more specific promise. The systems that have to keep those promises are where the next dollar of investment will do the most good.

For now, the demos keep ending where they do.

My last several posts have been circling around one idea, which is that when the customer, the product, and the company can operate as three peers rather than as two parties working around a passive third, something better becomes possible. I have called this value co-creation. The implications of this symbiotic relationship are many, and requires a fresh view of where to look.

Before I get to what co-creation produces, I want to be clear about what it actually is, because the word gets thrown around a lot in a way that makes it sound like something a company can decide to do. It is not. Co-creation is not a strategy the enterprise applies to the customer. It emerges, or it does not, from the three actors being in some kind of structured conversation with each other. The company cannot produce it alone. The customer cannot produce it alone, even with a great agent. The product certainly cannot produce it alone. The three have to actually be working together, and the trust framework that lets them do that has to be real. Without both of those conditions, what you get is a nicer-sounding version of the deflection era, not something structurally different.

When those conditions do hold, though, real things start happening that were not possible before. Let me try to describe three of them.

Product decisions start getting shaped by what customers actually value in use. That sounds like an obvious statement, but think about how product decisions actually get made in most companies today. Someone runs research. Another group looks at usage data. Then the product manager forms a hypothesis. The development team builds against the hypothesis, ships, and then watches to see whether the market responds the way the hypothesis predicted. The gap between what the product team thought and what customers actually value is where most product failures live. In a three-body arrangement, the customer’s agent is already telling you what the customer needed, and the product itself is already telling you what is actually being used and where. The gap does not close automatically, but at least you have the information required to close it without a six-month research cycle. Even faster when the product is not physical.

Pricing is the second thing that changes, and this one is more uncomfortable than most companies want to admit. Pricing has historically been an estimate on how the market values a product or service. It’s a well-informed guess in most cases, but still a guess, tested through what the market accepts. The customer knows what the product/service is worth to them, but rarely is an active participant in the pricing strategy. In intelligent product can model how much value it is producing, but has rarely had a voice in the pricing conversation. When both of them can contribute, pricing becomes something closer to a negotiation between what the customer is getting and what the company is actually delivering. That does not mean prices go down. Sometimes it means they should be higher, because the value is greater than the current price captures. What it means is that pricing gets grounded in something real rather than being set by whoever in the room speaks with the most conviction.

Service is the third thing and probably the one I am most confident about. For thirty years service has been measured on how fast it can close a ticket a customer already had to open. That model made sense in a world where the product could not signal problems on its own. In the near future, the product will already be flagging where users are getting stuck before those users have to write a ticket. The customer’s agent will already be flagging patterns of unmet expectation before they escalate. Service gets rebuilt around receiving these signals and acting on them. That is a big change for a function that has spent thirty years being reactive.

That said, co-creation is not simply a marketing message. There are a significant set of operational changes that will be required for enterprises to arrive at a technical architecture that enables the three-body relationship. And, technology alone may enable this shift, but it will also require a fundamental rewiring of how to operate in this new dynamic. That shift will be one of the biggest challenges for the next decade.

For a long time now I have been thinking about who actually knows what is going on inside a customer relationship. It’s not as straightforward a question as it seems to be. The customer operates from a context where they know what they meant to do. The company’s design parameters make it assume that it knows what it built and shipped. But neither of them, on their own, really knows what is happening in the middle, where the product/service generates value. That source knowledge sits with the product, and it is more than simple data and telemetry feeds.

The product knows how it is actually being used, not how someone hoped it would be used. It knows where users are getting stuck, which features people rely on, and which ones they ignore. When combined across multiple users, it sees patterns across everyone who touches it, which is something no individual user could ever see. If you wanted a source of truth about how a product is performing in the world, you would not go to the marketing team or the analytics dashboard. You would go to the product itself.

The trouble is that until very recently the product has had no way to tell us any of this. We have been reaching around it for years. Telemetry, session replays, support ticket analysis, voice-of-customer research. All of it useful, but none of it as good as what the product itself could say if it could talk. That is the shift I am sketching out here. Products are starting to be built with their own embedded intelligence, and that changes everything downstream of it.

Some of what I am describing has been happening in serious B2B relationships for years already. If you buy a jet engine or run a hospital MRI or operate a fleet of industrial robots, you already receive structured feedback from the product itself about how it is performing. Uptime, wear signals, performance drift. The manufacturer sees it, the customer sees it, and they operate on the same picture of what is happening. It works, and it has worked for a long time. What is new is that this pattern, which has been confined to high-stakes industrial contexts, is about to become the general case. Embedded AI is going to bring it to consumer goods, to enterprise software, to services of every kind.

When a product can speak on its own behalf, something changes about the customer-company relationship that I do not think we have fully reckoned with. The customer stops being alone with their own experience. The company stops being alone with its inferences. A third voice enters the conversation. Under a trust protocol like MyTerms, the product can share what it is seeing with both the customer’s agent and the company’s agent, on terms both sides have agreed to. Both get access to the origin source of insight for the first time.

That is what I mean when I say the product becomes a full actor in the three-body relationship I have been writing about. It is not just there to be used anymore. It has an account of what is happening, and both other actors can draw on that account directly. That is a different arrangement than the one we have been operating under for the last three decades, and I think it will produce healthier relationships for everyone involved.

There is a lot to work out. Who decides what the product pays attention to. Who it is allowed to talk to, and about what. Whether both sides can actually trust its voice, or whether it ends up quietly serving one of them. These are real design questions and we are not close to answering them. The people building the intelligence layer are mostly not thinking about the trust question, and the people thinking about trust are mostly not building at this layer yet.

What I feel certain about is that the shift is coming, and the version of customer experience we have been running for the last thirty years is not built for what happens next. For now, I would just point out that the product has been the origin source all along. It has known things about our relationships that neither customer nor company has been able to fully tap. We are about to hear from it directly. I do not think many of us are prepared for what it will have to say.

I’ve focused a lot on this Substack on the arrival of an empowered customer side agent. In many ways, that is the more interesting story, and it is where a lot of the real change is going to come from. But it is not the whole story. In the near future, the enterprise will meet the customer’s agent with one of its own, and the design of that agent will end up shaping every relationship it enters.

The enterprise agent is the general focus in today’s agentic AI conversation, and a lot of assumptions have been made on what the agent actually does. However, I think there is a massive gap that exists in the assumptions around enterprise side agents. That is a gap worth closing, because building the enterprise agent well is going to be one of the harder operational challenges of the next several years.

The work sorts into three areas. The first is representation. The agent will speak for the enterprise in every exchange with a customer-side agent, which means it will be the operational face of what the enterprise has committed to. Every claim it makes will be logged in the customer agent’s memory as a commitment the enterprise can be held to later. The design question is how much authority the agent carries. What can it commit to on its own. What does it need to escalate. What is it never allowed to promise. Those are strategic questions, not technical ones, and most enterprises have just started asking them.

The second area is protection. The agent has to look after the enterprise’s interests without losing the respect of the customer-side agent it is talking to. That balance is harder than most current agent frameworks are built for. If the enterprise agent is too accommodating, margins erode and precedents get set the enterprise cannot sustain. If it is too rigid, customer-side agents will note the pattern and route their relationships elsewhere. The right posture will vary by industry, by relationship stage, and by the specific commitments already on the table.

The third area is reciprocity. The customer agent will be sharing context under a trust protocol like MyTerms. The enterprise agent will need to receive that context in ways the customer agent can verify, act on it in ways the customer agent can audit, and contribute context back where doing so would strengthen the relationship. While secure API connectors have existed for a long time in the B2B world, the concept will need to radically evolve to be agentic, especially in the B2C world. This is not how enterprise communication with customers has ever worked. It has been almost entirely one-directional. Adding to this is that there will be a third, agentic agent, that of the product/service. The enterprise agent has to be built to handle these new relationships for that from the start.

There is a further layer of complexity most current thinking is missing. The enterprise agent is not going to be operating in a two-body relationship. It will be operating in a three-body one. I first proposed this as the three-body problem of customer experience last year, and returned to it more recently with the three bodies, four forces model that has been a core thesis of this Substack. The customer is one actor. The enterprise is a second. The product or service the enterprise offers is the third, and in the near future the product will bring its own intelligence into the conversation. What the product is learning about how it is being used, where it is delivering value, where it is falling short. The enterprise agent has to be designed to receive that signal, weigh it against what the enterprise itself is committing to, and represent the whole picture accurately to the customer agent it is negotiating with.

That is a level of design complexity that current agent projects are likely not built for. Most enterprise agent efforts today assume a two-body model. A customer and an enterprise, mediated by a bot. The three-body reality is going to demand something structurally different, and the enterprises that recognize that early will end up with agents that hold together across the range of situations they are going to encounter. The ones that build for the two-body version will find their agents contradicting the product, mismanaging expectations, and eroding standing without anyone quite understanding why.

The infrastructure to support any of this does not currently exist at most enterprises. There is no operating model for what the enterprise agent should be authorized to do. There is no legal framework for what it can commit to on the enterprise’s behalf. There is no organizational home for the function that owns how it behaves. Each of these will have to be built.

The real question is not whether the enterprise will have an agent. It is what the enterprise wants that agent to stand for, and whether the enterprise is willing to design it for the actual complexity of the relationship it is about to enter. That complexity includes a product body that is going to have plenty to say for itself, and I want to spend the next piece on what that product body actually contributes and how the enterprise should be preparing to receive it.

For most of the last century, brand has been the organizing concept of marketing. Every mature marketing organization is built around the idea that the enterprise controls what it stands for and how customers come to understand that. The channels, the timing, the language. All of it has been the enterprise’s call. That control is going away, and the version of brand coming to replace it does not fit neatly inside the marketing conversation we have been having for a hundred years.

Brand has always sat in the space where three things collide. The claims the enterprise makes about itself. The customer’s actual experience of what they bought. The product’s real behavior once it is out in the world. The problem is that these three things have never really been in conversation with each other. The enterprise made its claims. The product either held up or it did not. The customer formed impressions somewhere in the middle, and shared some of them with friends. It has been slow, it has been indirect, and only one side of the exchange has ever had structured leverage.

In the three-body model I have been developing across my last several posts, brand becomes something quite different. It becomes the signal layer running between the customer, the product, and the organization, and all three of them draw from it and contribute to it continuously.

Here is what that looks like. The customer’s agent will notice how the enterprise’s claims are landing over time. Which promises are holding. Which have been discounted as noise. Which are being cited approvingly to other agents. The product will have its own tuned intelligence, and it will contribute what it is learning about how it is actually being used and where it is falling short. The enterprise will bring what it has committed to, what it is deciding, and what it wants to become. All of that flows through the brand signal, and each body uses what the others contribute to figure out what to do next.

What sets brand apart from the other layers in this infrastructure is that it carries two very different kinds of value at the same time. There is the quantitative side, which is what agents will actually work with. Reputation over time, a running record of commitments made and kept, the specific claims that have been verified and the ones that have not. Then there is the qualitative side, which is what humans still experience directly. The meaning of the brand. Its character. Whether it feels like something you want to be associated with. These are not the same kind of thing, but brand is where they meet, and the enterprise cannot operate in the new era without both being present at once.

The enterprise will learn what its brand actually is from the signal itself, not from a focus group or a survey or its own guess about how customers feel. The customer’s agent will negotiate on the basis of what the signal shows about the current state of the relationship, not on the marketing narrative. The product will evolve in response to what the signal is telling it about what customers actually care about. Brand stops being a story the enterprise tells about itself and starts being something the whole relationship produces together.

This is the kind of co-creation the deflection era could not have generated. Brand does not disappear in the agentic era. It becomes part of the infrastructure. And the enterprises that learn to build for both sides of what it now carries, the machine-readable and the human-felt, will end up with a version of brand that a hundred years of marketing has been trying to describe without ever quite reaching.

In the last piece, I argued that the enterprise context graph being built across the industry is only one-third of the infrastructure the agentic era will require. The customer and the product each bring knowledge and context of their own, and the value that emerges when all three contribute is qualitatively different from anything a single body can produce. The customer’s contribution is the one I want to focus on here, because it is the most important of the three and the one that is often overlooked given the history of surveillance being the default posture between enterprise and customer.

The reason the customer contribution matters most is straightforward. The customer is the only body in the relationship that experiences the full arc of it. The organization knows what it built and why. The product knows how it is being used. Only the customer knows what any of it actually means. What they were trying to accomplish, whether they succeeded, and what they wished the enterprise had asked them about before making the decisions it made. All of that is context, and it has never been available to the enterprise on terms either party could trust.

That is about to change. Customer-side agents, operating under a trust protocol like MyTerms, will make the customer’s own account of the relationship available to the enterprise as a first-class contribution to the context layer. MyTerms provides the missing piece the industry has been circling for years. A bilateral agreement, machine-readable and enforceable, that specifies what the customer is willing to share, what the enterprise is allowed to do with it, and what the customer expects in return. With that protocol in place, the customer’s agent will share context the enterprise has never had access to at any point in the history of customer experience.

What the enterprise will receive is not another data feed. It will be a structured account of the relationship from the customer’s own perspective. Why the product was purchased. What problem it was meant to solve. Whether that problem was solved. What has changed in the customer’s circumstances since. What competing options are being considered. What the enterprise could do to strengthen its standing over time. That is a completely different order of information from anything the enterprise’s own instruments have ever produced, because it is the customer’s own account rather than the enterprise’s interpretation of behavior. The customer will have latitude, through a MyTerms contract, in the depth and breadth of the information to share with both the enterprise and the product/service.

The context also compounds in ways the enterprise-side context cannot. The customer’s agent will remember every interaction with every vendor the customer engages with. It will compare experiences across the customer’s full commercial life and surface patterns the customer might never have articulated on their own. When it shares context with the enterprise, it is offering not just what the customer knows about this relationship, but what the agent has learned across many.

The implications will be significant and welcome. Product decisions will be shaped by the actual meaning customers are deriving from what they bought. Pricing will be shaped by value customers describe in their own terms. Service will be shaped by the customer’s own account of what would have made an interaction go better. Marketing will be shaped by claims the customer’s agent can verify against the customer’s own record of experience.

When customer-side agents begin participating as full peers under MyTerms, the enterprise will have access to a kind of understanding of its customers that thirty years of surveys and telemetry never came close to producing. The enterprises that build to receive it will operate with an intelligence the old era could not have generated, and the customers themselves will finally be in a position where their own account of the relationship is what shapes the decisions that affect them. That is a healthier commercial arrangement than either side has ever operated under, and it is coming into view faster than most of the current conversation is registering.

The conversation about knowledge graphs and context graphs in enterprise AI has matured quickly over the last several months. With industry analysts projecting that roughly one-third of enterprise applications will embed agent-based intelligence by 2028, the underlying data architecture required to power these systems is shifting. It is no longer a niche discussion; the market for enterprise knowledge graphs is actively accelerating, projected to expand from roughly $1.9 billion today to nearly $10 billion over the next several years.

Major hyperscalers are moving decisively into the space. A prime example is Amazon’s entry with AWS Context, a new knowledge graph service designed to dynamically surface organizational intelligence that continuously learns and sharpens through agent usage over time. As platform companies like Atlan, Glean, Neo4j, and a growing roster of data infrastructure providers converge on the same idea, a fundamental truth has emerged: AI agents cannot operate reliably without structured, governed context about how enterprises actually work.

The distinction between the two types of graph is also emerging across enterprise architecture conversations:

• The Knowledge Graph: Maps entities and their relationships at design time. It answers what things are, where data resides, and how corporate assets connect.

• The Context Graph: Extends that exact same structure at runtime, capturing decision traces, data shelf life, and behavioral policies. It answers why decisions were made, how the enterprise actually operates, and what tribal knowledge sits between traditional systems of record.

Both are emerging as critical infrastructure for the agentic era, and both are being built with real urgency inside enterprises right now.

But, in the conversations happening today, these graphs are largely built as internal, enterprise-only artifacts. That is a natural starting point, because the immediate, burning problem is helping corporate agents understand internal workflows. It is also, from a holistic perspective, structurally incomplete. If the three-body model of customer experience holds, then each of the three bodies will have its own unique knowledge and its own runtime context to contribute to the relationship. The graphs being engineered today are capturing only one-third of what the model will ultimately require.

Let’s quickly review how I’ve been thinking about the three bodies that produce durable customer value as the organization, the customer, and the product or service that binds the relationship. Each has knowledge and context native to its domain:

1. The Organization

The organization has structural knowledge about its products, its policies, its pricing, and its capabilities. It maintains runtime context about why an account executive made a specific concession, how escalations flow through support tiers, and where human expertise lives. This is what the current context graph conversation is about, and enterprises are right to build for it. But it remains an isolated silo.

2. The Customer

The customer possesses knowledge and context of a radically different kind. The customer knows what they were trying to accomplish when they used the product or service. They know what worked and what failed. They know what alternative solution they would have chosen if they had been better informed, how the product actually fits into their daily life or workflow, and which of the enterprise’s brand promises landed versus which ones fell short. Their context is the living decision trace of their own life as they navigate the vendor relationship.

Under a trust protocol like the developing IEEE 7012 MyTerms standard, this rich customer knowledge and context will become portable, structured, and available to the enterprise on machine-readable terms the customer has explicitly set rather than harvested silently and inaccurately through third-party cookies and fragmented telemetry.

3. The Product or Service

The product body is the one the current enterprise conversation is largely overlooking. As AI gets embedded into products themselves, the product will no longer be a passive instrument observed from the outside. It will feature its own localized AI model tuned to the product’s explicit purpose with an architecture capable of generating intrinsic product insights.

This is a fundamentally different kind of signal from the telemetry the industry has spent the last thirty years collecting. Telemetry is a lagging indicator; it is what a monitoring system observes from the outside (e.g., the user clicked a button three times). Product-side AI feedback is what the product itself has to say about its own life in the wild (e.g., the user is experiencing acute friction executing this specific data transformation workflow). This real-time edge context is structured so that both the customer and the enterprise can act on it immediately.

A Fully Enabled Graph

When all three bodies emit both design-time knowledge and runtime context, and when a trust protocol like MyTerms makes that emission collaborative rather than extractive, something entirely new becomes possible.

With a collaborative approach, the knowledge graph is no longer just what the enterprise knows about its own backend operations. It also incorporates what the customer, the product, and the organization together know about the relationship they share. The context graph is no longer just the internal decision traces of corporate software. It becomes the holistic decision trace of the entire three-body system, dynamically blending the customer’s evolving priorities, the product’s actual edge behavior, and the enterprise’s operational constraints.

The value that emerges from this synthesis is not incremental; it is a category shift. It represents a layer of shared understanding that could never exist inside a single organization’s silo. The customer’s agent will know what the user intended to do. The product’s internal model will know what actually happened at the execution layer. The organization’s graph will know what the enterprise committed to deliver and why. When all three are in a structured, multi-agent conversation with one another, they co-create value based on absolute ground truth.

This will fundamentally rewrite what enterprises build and buy over the next several years. The context layer cannot remain an internal-only asset. It must evolve into a collaborative surface where enterprise, customer, and product all contribute what they know under a robust governance framework.

The organizations that recognize this shift early will design for the three-body pattern from day one. The ones that treat the context graph as a glorified corporate database will find themselves completely rebuilding their data layer once user-side trust protocols mature and customers arrive at the relationship with structured representation of their own.

The knowledge and context graphs being deployed right now are the beginning of the infrastructure shift, and not the end of it. What comes next is an ecosystem that treats all three bodies as equal contributors to the shared understanding that the agentic era demands. When that arrives, the value created between customer and enterprise will be fundamentally different from anything the old cost-deflection era could produce, because for the first time, both sides will be building on the exact same graph of what the relationship actually is.

The idea of “owning the customer” has been the organizing metaphor of customer experience for as long as the discipline has existed. Account ownership, customer ownership, segment ownership. The language runs through every CRM implementation, every quarterly business review, every leadership scorecard. It made sense inside the enterprise, but it never made sense to the customer, because no customer has ever chosen to be owned. The enterprise got away with the fiction for thirty years because the customer had no structured way to push back. Customer-side agents are going to remove that limitation, and once they do, ownership will become a relic of a world where only one side of the relationship was allowed to bring structure to it.

What will replace ownership in the agentic era is something like a reputation ledger, and it will work fundamentally differently. Ownership was something the enterprise claimed. A reputation ledger is something the customer’s agent will keep, and the entries in it will not be permanent claims. The ledger will behave more like a running account than a title deed, and the customer’s agent will keep the books.

The best way to think about it is as a ledger the customer’s agent will maintain for every enterprise it interacts with on behalf of the customer. Every commitment the enterprise honors will add to the balance. Every promise it fails to deliver on will subtract from it. Every act of reciprocity, such as sharing useful context back, honoring the customer’s stated terms, or acting on feedback in a way the customer’s agent can verify, will add value. Every act of extraction, every dark pattern, every unexplained fee, will subtract value. The customer’s agent will keep this ledger in a way no individual customer ever could, because agents will remember everything, compare across vendors, and share notes with agents representing other customers.

The implications of the reputation ledger for the CX operating model will be significant, and most enterprises are underestimating them.

Marketing’s job will no longer be to manufacture preference. It will be to produce claims the customer’s agent can verify, so that positive claims add value to the ledger rather than being dismissed as noise. A campaign that overstates a capability will not just fail to convert. It will actively subtract value from the ledger, because the agent will notice the gap between claim and delivery and score the enterprise accordingly.

Product’s job will no longer be to ship features and let marketing tell the story. It will be to publish capabilities in a form the customer’s agent can evaluate, and to hold the delivered experience accountable to those capabilities in real time. The ledger will grow in value when the product does what it said it would do. It will shrink when it does not.

Support’s job will shift the most, because support is where the ledger will be tested most often. Every interaction will be a chance to add or subtract to the balance. In the deflection era, support was measured on how quickly it could close a ticket. In the ledger model, support will be measured on whether the resolution added to the value balance or subtracted from it. That is a fundamentally different scorecard, and it will make service the connective function it was always trying to be but was never structured to become.

Sales will look the most different, because the transaction itself will be the moment when the ledger opens. The terms agreed to, the promises made, the commitments booked. All of them will become entries the customer’s agent will check against reality over the life of the relationship. Sales will stop being the function that closes deals and become the function that opens accounts the rest of the enterprise will spend years honoring.

The through-line across all four functions will be that reputation is earned in every interaction and lost in every misalignment. No function will get to declare it has been earned. No leader will get to claim their team owns the customer. The customer’s agent will keep the books, and the enterprise’s job will be to make sure the running balance is one worth honoring.

The leaders who internalize this early will stop asking who owns the customer, and start asking how to build relationships worth co-creating value in, on both sides of the ledger.

Something has been shifting in the customer experience conversation over the last year, and most of the people having the conversation have not yet put it together. The full narrative is scattered across different discussions. Customer-side agents are being debated in privacy and consumer circles. Context graphs are being built by enterprise AI teams. Trust frameworks are being designed in standards bodies. Co-creation is being talked about as a strategy without anyone explaining how it would actually work. All of these conversations are describing different parts of the same model, which is what I’ll walk through in this piece.

The model rests on an observation that has been getting clearer as the parts have come into view. The customer is going to stop being something the enterprise does things to, and start being something the enterprise does things with. Every other shift in the conversation follows from that one.

The structure of the model is the three-body frame I introduced a while back, although I’ve shifted the definition of the tree bodies to reflect a more comprehensive operating model. The three bodies that produce durable customer value are customer, product or service, and organization. Each has its own gravity, generates its own context, and evolves on its own clock. For the entire history of customer experience, these three bodies have been navigating each other with no medium fast enough or rich enough to keep them aligned. The customer experiences a product the organization built on its best guess about what the customer would want. Each body senses the others through narrow channels, and context gets lost with every translation.

The arc of the last several months has been describing what happens when those channels widen and become structured. The change involves four different kinds of work happening between the three bodies, and each kind of work has a different character. Agents are the medium that carries the relationship. Context is the substance that flows between the bodies. Trust is the framework that governs how the substance moves. Co-creation is the result the system produces when the other three are working together.

Agents are the medium that carries the relationship between the three bodies. Customer-side agents will hold the customer’s context, enforce the customer’s interests, and negotiate on the customer’s behalf. Enterprise-side agents will hold organizational context, enforce internal policy, and act on behalf of the company. The product layer will be increasingly instrumented to emit usage signals, telemetry, and behavior data that agents on either side can act on. None of these participants will operate on reputation alone, because reputation does not survive contact with the volume and speed of agent-mediated interaction. The speed and structure of an agent-mediated relationship is different in kind from a human-mediated one, and that difference will show up at every other point in the model.

Context is the substance that flows between the bodies. Agents cannot do useful work without it. The customer-side context payload, the product telemetry, and the enterprise context graph are three sources of the same substance, and the work of the next several years is going to be making sure all three are emitting, structured, and legible to the agents that need to act on them. The companies that get this right will be operating with a much more complete picture of the customer than the deflection-era enterprise ever had access to.

Trust is the framework that governs how the substance flows. Context can only move safely between the bodies when both sides can verify what the other is doing with it. That verification has to be continuous, machine-readable, and provable in real time, because the parties involved are agents moving at machine speed. The framework has to make three things continuously verifiable: that the agents are who they claim to be and operating within their stated boundaries, that the context flowing between them is protected throughout the interaction, and that everything that happened leaves a tamper-proof record either side can audit later.

This is the area I have been watching most closely, because it is where the most important infrastructure for the entire model is being built right now. The contract side of the framework is being developed through standards like MyTerms (IEEE 7012), where the customer brings their own terms to the relationship rather than accepting whatever the vendor’s lawyers wrote. The infrastructure side is being built by companies like Opaque, whose confidential AI platform uses hardware-backed attestation, cryptographic enforcement, and tamper-proof audit logs to make trust between agents verifiable rather than assumed. The contract and the infrastructure together are what convert trust from a marketing claim into an operational guarantee.

Co-creation is the result the system produces when the other three forces are working together. The customer-company relationship stops being transactional and becomes continuous, bilateral, and generative. The customer shares context that previously stayed inside their head because they can see what the enterprise can and cannot do with it. The enterprise commits to outcomes that previously felt too risky to promise because it can prove what was delivered. The product roadmap improves because real usage signals flow back in a form the enterprise can act on. Pricing becomes fairer because both sides can see the value being created over time. Service stops being a defensive function and becomes the connective layer where new value is generated continuously rather than reconciled occasionally.

That is the shape of the model. Three bodies, four forces between them, and an operating model for customer experience that looks fundamentally different from what the deflection era produced.

The deflection era tried to run this same system without any of the forces in place. Customer-facing channels stood in for customer-side agents, siloed systems of record stood in for flowing context, consent banners and unread contracts stood in for verifiable trust, and loyalty programs and lifecycle marketing stood in for co-creation. Each of those substitutes did some of the work for a while, but the substitutes are reaching the end of what they can carry. The enterprises that try to bolt agentic AI onto a stack designed around those substitutes are going to find that the stack does not support the load.

The organizational implications are the part most CX leaders are still underestimating. The org chart was built to process customers as endpoints in a journey the enterprise designed. When the customer becomes a peer node in a relationship the enterprise no longer fully controls, the org chart starts to break down. The idea of ‘owning’ the customer loses meaning. The functional boundaries between marketing, product, sales, and service soften, because the work itself steps beyond those operational boundaries. The trust framework needs an organizational home that does not currently exist at most companies. Titles will evolve because the work underneath them will evolve.

The point of all of this is not the technology. The technology is what makes the medium possible. The point is what the medium unlocks, which is a fundamentally different economic relationship between enterprises and the customers they serve. A relationship in which both sides can invest more because both sides can verify more. One in which value is created continuously instead of extracted episodically. One in which the customer is represented in a structured way for the first time in the history of commerce, and the enterprise is held accountable in a structured way for the first time in the history of customer experience.

The shift is going to take several years to fully land. The leaders who recognize the model now and start operating on it before the org chart catches up will be the ones running the businesses that compound customer value through the rest of this decade. The ones who wait for the new model to be packaged and sold to them will spend the next several years explaining why their CX investments are not landing, and why their customers, increasingly equipped with agents the enterprise was not prepared to negotiate with, are no longer behaving the way the journey map predicted.

Customer, product, organization. Three bodies. Agents as the medium. Context as the substance. Trust as the framework. Co-creation as the result.

In the last piece I argued that verifiable trust is going to be the precondition for value co-creation, and that co-creation is the operating model that will define the next era of customer experience. Now let’s go deeper into what that change is going to look like inside the enterprise.

For as long as customer experience has been a discipline, the org chart has reflected a single assumption. The customer is an endpoint. Marketing acquires them. Sales closes them. Service supports them. Product builds for them. Each function has its own metric, its own budget, and its own definition of success, and the customer moves through the org chart as a unit of work that gets handed off between functions. The journey map is just the visual layer on top of this assumption. The deeper artifact is the org chart itself.

Looking back across several waves of enterprise software, this assumption has been the structural wall every customer-centric initiative has eventually run into. The CRM era promised a unified view of the customer and tried to deliver a unified database with the same functional silos still operating around it. The customer experience suite era promised journey orchestration and delivered better tooling for the same handoff model. Each generation got closer to the customer without actually moving them out of the endpoint position, because moving the customer out of the endpoint position is not a software problem. It is an organizational design problem, and software cannot solve it from underneath alone.

Customer-side agents are going to force the issue in a way previous waves did not. When the customer arrives at every interaction represented by an agent that holds their context, enforces their interests, and negotiates on their behalf, the enterprise can no longer treat them as the recipient of a process. The customer becomes a peer node in the relationship, and the org chart that was built to process endpoints does not know what to do with a peer node. That is the redesign question.

A few principles will define how that redesign actually unfolds.

The idea of “owning the customer” inside the enterprise is going to lose meaning. Account ownership, customer ownership, segment ownership were all artifacts of a world where the enterprise was the only side bringing structure to the relationship. When the customer brings their own agent and their own context, what the enterprise can earn is standing, not ownership. The functions that succeed in the new model will be the ones that learn to earn standing with the customer’s agent rather than insisting on ownership of a customer who is no longer asking permission to manage their own relationship.

The functional boundaries between marketing, product, sales, and service are going to soften, not because anyone decides to break them down, but because the work itself stops respecting them. When all four functions are operating against a shared context graph that includes the customer node, the handoff model that justified the boundaries becomes friction rather than structure. The functions still exist, but the question of which function owns which stage of the journey becomes the wrong question, because the journey is no longer the model.

The trust framework underneath the relationship is going to need an organizational home, and at most companies that home does not currently exist. The function that owns how customer context is received, governed, audited, and reciprocated is a function that has not been built yet at most enterprises. The principle that matters is that this function is going to be load-bearing, and treating it as a side responsibility of an existing team is going to underweight it.

Titles will evolve, because the work underneath them will evolve. Chief Customer Officer does not disappear, but the work that role does will look different when customers are no longer endpoints to be experienced and become peers to be co-created with. VP of Customer Experience does not disappear, but the experience being designed will no longer be a journey through stages. The titles will catch up to the work, the way titles always do. What the leaders in those roles do every day is what will shift first, and the language will follow.

The point I am making is that this is not just a technology tooling question alone. It is an organizational redesign question, and the redesign will run on principles long before it runs on a new org chart. The leaders who recognize that early, and start operating on the new principles before the chart catches up, will be the ones running the businesses that compound customer value through the rest of this decade. The ones who wait for the chart to change first will spend the next several years explaining why their CX investments are not returning the results that they had expected.

In the last piece I argued that the customer-shaped hole in the enterprise context graph can only be closed under a trust framework, something that is in active development. This piece is about what that framework is going to unlock.

Trust in the agentic era is going to be structurally different from trust in any prior era of customer experience. For thirty years it was carried by people, paperwork, and slow-moving processes. That arrangement was always more fragile than the industry liked to admit, and it is not going to survive contact with agents acting on either side at machine speed.

When a customer’s agent and a company’s agent enter a relationship, neither will be able to operate on reputation alone. The customer’s agent will have fiduciary obligations and cannot share context that might be misused. The company’s agent will have obligations to the enterprise and cannot accept context it cannot verify. The trust between them will have to be provable in real time, not assumed and reconciled later.

What is going to be needed underneath the relationship is a layer that makes three things continuously verifiable. The first is that the agents on both sides are who they claim to be and are operating within their stated boundaries. The second is that the context flowing between them is protected throughout the interaction, not just at the start and the end. The third is that everything that happened leaves a tamper-proof record either side can audit later. Attestation, enforcement, audit. Those are the foundational concepts, regardless of how the technology layer evolves to deliver them.

This matters because of what it is going to unlock, not because of how it will work. The difference between a relationship with verifiable trust and one without is not going to be incremental. It will be the difference between extracting value from customers and co-creating value with them.

A relationship without verifiable trust forces both sides to hold back. The customer holds back context because they cannot be sure how it will be used. The company holds back commitments because it cannot be sure what the customer will hold them to. Both sides operate behind narrow guardrails, which means the value that gets created is bounded by what each side is willing to risk in the absence of proof. That is the model that produced the deflection era of customer service, the consent-banner era of customer privacy, and the loyalty-program era of customer marketing. All three are different faces of the same underlying problem, which is that nobody fully trusted anybody, so nobody fully invested in the relationship.

When trust becomes verifiable, the dynamic will invert. The customer will be willing to share context that previously stayed inside their head because they can see exactly what the company can and cannot do with it. The company will be willing to commit to outcomes that previously felt too risky to promise because it can prove what was delivered. The product roadmap will improve because real usage signals can flow back in a form the enterprise can act on. Pricing will become fairer because both sides can see the value being created over time. Service will stop being a defensive function and become the connective layer where new value is generated continuously rather than reconciled occasionally.

Verifiable trust is going to be the precondition for co-creation, and co-creation is going to be the precondition for the kind of compounding customer value the deflection era never produced and never could. The leaders who win this era will be the ones who recognize, while it is still early, that trust is no longer going to be something you claim. It will be something you build, prove, and deliver, continuously, between two parties who can both verify it. The companies that start positioning for that shift now will be defining the operating model their competitors are still trying to understand. When that condition holds, value co-creation stops being a strategy deck and starts being the way the relationship actually works.

There’s been a real and useful conversation building over the last several months about context graphs and their role in enterprise AI. Gartner and Forrester have identified context graphs as foundational infrastructure for agentic AI, with a projection that by 2029, 80% of AI agent platforms using reasoning models will have aligned context layers in place, compared to fewer than 10% today. Microsoft just launched IQ at Build 2026 as an enterprise intelligence layer that grounds AI agents in workplace data and business systems. Glean, Atlan, Publicis Sapient, and a half dozen others are all converging on the same idea: AI agents need a structured understanding of how the enterprise actually decides, escalates, and resolves things, not just a flat snapshot of what’s in the systems of record.

I think the conversation is correct on the substance and structurally incomplete on the scope. Every framing I’ve seen treats the context graph as an enterprise-internal artifact. The decision threads in Slack, the escalation patterns in the ticketing system, the tribal knowledge that lives between employees. All of that matters, and the enterprise is right to be building infrastructure to make it queryable. But the graph stops at the moment the customer enters the picture, and that is where the most valuable context in the entire system actually lives.

The richest context about your product or service is not held inside your company. It is held by the people who actually use what you sell.

That sentence sounds obvious when you read it, and the obviousness is exactly the problem. CX has organized itself for decades around customer journey maps that treat the customer as a stage-completion function. Did they convert. Did they activate. Did they expand. Did they renew. In the previous piece I argued that the journey map was always a fiction the enterprise told itself. The deeper fiction underneath is that the customer is a callable function in your process rather than the most valuable node in your graph.

The customer holds the why. They know what they were trying to accomplish when they bought your product. They know which features they actually use and which ones they ignore. They know which workarounds they invented when your product didn’t do what they needed. They know how your service interaction made them feel about renewing. They know how your pricing landed against the value they actually derived. None of that lives in your CRM. Some of it lives in your support tickets, albeit captured in opaque ways. Most of it lives in the customer’s head, and it has been functionally invisible to the enterprise for the entire history of CRM/CX as a discipline.

Customer-side agents are about to change that, and I think the implication for the context graph conversation is bigger than the current focus is treating it. When a customer deploys an agent that represents them across the vendors they buy from, that agent accumulates exactly the kind of structured, queryable context that enterprises have been trying to manufacture internally. It knows what the customer wanted, what they got, what worked, what didn’t, and what they would have preferred. It carries that context in a form that can be shared, in whole or in part, with the vendors who have earned the right to receive it. That is the customer’s missing node, finally legible.

The hard part is what comes next, and it is not technical. Companies have to fundamentally reposition where the customer sits in their operating model. As long as the customer is a callable function inside a journey map, there is no place in the architecture to receive what their agent is offering. The customer has to move from being a target of the enterprise’s process to being a peer node in the enterprise’s graph. I think that is an organizational redesign question more than a data engineering question, and it is going to be the harder lift for most CX leaders to make.

It also requires a trust framework, because customer context only flows when both sides have a durable framework, like contract, that governs it. Consent banners are unilateral, brittle, and largely performative. What is required is a bilateral agreement, machine-readable, persistent, and auditable, that defines what the enterprise can do with the customer’s context, how long it can hold it, and what the customer gets back in return. That work is underway in standards bodies and in adjacent communities (MyTerms being one of them), and I think it becomes essential infrastructure for everything the agentic CX conversation is currently hand-waving past.

When both sides bring context into a relationship that has a contract underneath it, value co-creation stops being a strategy deck and starts being an operational reality. The enterprise’s context graph gets the missing node. The customer’s agent gets a counterpart that can actually respond to what it knows. Both sides end up holding a richer, more useful, and more honest view of the relationship they are actually in.

The context graph conversation is the right conversation. It just needs to include the customer. The technology is finally good enough to make that possible, and the leaders who do the work to receive what their customers’ agents are about to offer will end up running businesses that compound value in ways their deflection-era predecessors never could.

Every customer journey map I’ve ever seen drawn assumes the enterprise is the cartographer, charting a pathway for an uninformed customer. Awareness, consideration, purchase, onboarding, adoption, renewal. The stages are tidy, the arrows point in one direction, and the map gets pinned to the wall in the CX war room as if customers consult it on their way through the funnel. They don’t. They never have. I’ve come to think the journey map was always a convenient fiction the enterprise told itself so that marketing, sales, and service could coordinate handoffs across a relationship the customer was actually running on their own terms.

In the last piece, I argued that agentic AI will move CX from deflection into co-creation, where both sides keep adding value to the relationship after the sale rather than periodically reconciling its failures. That argument has a quiet assumption baked into it, and I want to pull it out into the light. Co-creation only works if the enterprise accepts that the customer is now an equal party in the relationship. Looking at the trend-line, the coming arrival of customer-side agents is what makes that shift impossible to ignore.

A customer-side agent doesn’t enter your funnel. It enters your product, your pricing page, your terms of service, your competitor’s product, your competitor’s pricing page, three independent review sources, two regulatory filings, and a Reddit thread from 2024, and it does all of that before the customer it represents has even decided whether to engage with you directly. The agent carries fiduciary trust on behalf of its principal. Its job is not to be persuaded by your marketing, it’s to optimize an outcome for the person it works for. I think that changes the structure of every interaction the enterprise has been organized to run.

The implications take a minute to settle. In a B2B context, the agent shows up to a negotiation with deeper knowledge of your product than your own sales team has, because it doesn’t have a quota to achieve and it doesn’t have a script follow. It has read your documentation more carefully than your customers ever did. In some cases, the customer’s agent has cross-referenced your SLAs against your historical incident reports. It has compared your renewal terms against the three vendors who would happily replace you. The information asymmetry that used to favor the enterprise is now inverted, and I think the inversion is central to realigning assumptions. No amount of sales enablement closes that gap, because the gap isn’t about training, it’s about whose interests the intelligence is optimized for.

The B2C version is even more interesting, because there’s no sales cycle to soften the moment of transaction. When I look at how this plays out for a consumer brand, there’s no quarterly business review, no procurement window, no relationship manager working the account. The customer’s agent just arrives, fully briefed, with a clear instruction from its principal. Find me a better mortgage. Switch my insurance if the renewal exceeds last year by more than five percent. Buy the running shoes I liked last time unless something measurably better has appeared at the same price. The brand never gets a meeting. It gets a verdict, delivered at machine speed, against criteria the customer set and the agent enforced.

That moment is where every consumer marketing assumption I’ve watched the industry build for a generation runs out of road. Loyalty programs, brand affinity, retargeting, lifecycle email, the whole apparatus was designed to influence a human who could be nudged. An agent acting on fiduciary instructions cannot be nudged. It can only be convinced by evidence its principal would value, and it will collect that evidence from sources the brand has no relationship with and no ability to shape.

This is where I think most CX strategies will break, and where the next era of value creation actually begins. The only durable response to an informed, fiduciary, agentic counterparty is to make the long-term value of what you sell genuinely defensible. Not the marketing narrative around the value, the value itself. If your product works, if your pricing is fair, if your service actually solves the problem, the customer’s agent will surface that and reinforce the relationship. If any of those things are weaker than the story you’ve been telling, the agent will surface that too, and it will route its principal toward whoever can do better.

I know this can sound threatening if you read it as the end of marketing leverage. I don’t think it is. I think marketing itself is being reshaped, and the reshape is healthier than the function’s recent decade has been. The narrative center of gravity moves from the promise of value to the delivery of value, and the leverage moves with it. The brands that win in an agent-mediated world are the ones whose marketing is grounded in what the product actually does, what the pricing actually costs over time, and what the service actually resolves. That’s still marketing. It’s just marketing that has to be true to be effective, because the customer’s agent will check.

I think this is the beginning of a much healthier economic relationship between enterprises and the customers they serve. The companies that lean into this deepen their commitment to long-term product quality, transparent pricing, and service that compounds value over time. Marketing becomes the function that translates real value into language the customer’s agent can verify, rather than the function that manufactures preference in the absence of verification. The customers who deploy agents on their behalf get representation in a relationship where they have historically been outgunned. Both sides win, and the win is structural rather than promotional.

The journey map was the enterprise’s fantasy of control. The customer-side agent is the customer’s restoration of a durable balance in the relationship. The leaders who internalize that shift early will build the businesses that are still standing when the dust settles, and they will do it by competing on the only thing that survives an agent-mediated relationship, which is whether what you sell is actually worth what you charge for it.

The brand promise is made in marketing, priced in sales, and tested in service. That sequence has been true for as long as customer experience has been a discipline, and yet most enterprises still invest as if the promise is fulfilled the moment the contract is signed, or product purchased. Marketing gets the budget for the promise, sales gets the comp for the close, and service gets the headcount cap and the deflection target. The part of the journey where brand value is actually proven, the long arc after the transaction where customers decide whether they got what they were sold, sits inside the function with the least strategic capital in most organizations. The math here has always been backwards, and the technology of the last thirty years gave the enterprise no real reason to fix it.

In the last piece, I argued that AI lets service stop being a translation layer absorbing the enterprise’s failures of imagination, and start operating as the nervous system of the enterprise, sensing where value is leaking and routing the signal to wherever the response actually has to come from. That reframes service from a cost center into a context-routing function. The next move is bigger. Once context is moving across the enterprise in real time, the relationship between the company and the customer changes shape entirely. Service stops being the place where oneway, company-side, value gets defended and becomes the place where value gets co-created, both company and customer benefit.

This shift is what agentic AI actually unlocks, and most CX leaders aren’t yet planning for it. The agentic conversation today is dominated by automation framing: agents that resolve tickets, agents that handle returns, agents that complete tasks without human escalation. That framing keeps service trapped inside the deflection mindset wearing a new technology coat. The more interesting move is the one the technology genuinely makes possible. When AI agents operate with full enterprise context on behalf of the customer, and when the customer’s own agents operate with full personal context on behalf of the buyer, the post-transaction relationship stops being transactional at all. It becomes continuous, bilateral, and generative. Both sides are now in a position to keep adding value to the relationship rather than periodically reconciling its failures.

That is co-creation, and it is structurally different from anything CX has been organized around before. Co-creation assumes the customer is an active participant in defining what good looks like, not a recipient of what the company decided good was at the time of sale. It assumes the product roadmap, the pricing model, the service experience, and even the contractual terms are living artifacts shaped by what the enterprise learns from how customers actually use what they bought. Agentic infrastructure makes this operationally feasible at scale for the first time. The customer’s agent can negotiate terms, surface unmet needs, flag friction, and request adjustments. The enterprise’s agents can respond with context that spans product, finance, and operations rather than the narrow script the service rep was handed in 2015.

The economic implications of this are worth sitting with. A deflection-oriented service organization extracts value from each interaction by minimizing its cost. A co-creation-oriented service organization compounds value from each interaction by feeding it back into the things the customer actually paid for. One model treats every contact as a tax on operations. The other treats every contact as an investment in the relationship’s future yield. Over a multi-year horizon those two models diverge dramatically, and the gap shows up in retention, in expansion, in pricing power, and in the kind of brand equity that no amount of marketing spend can manufacture after the fact.

This is also where the strategic positioning of CX changes inside the enterprise. For decades, service has reported into operations because the dominant frame was containment. Co-creation pulls service toward the strategic core, alongside product and finance, because the function is now generating the signal that shapes what the company sells next, how it prices it, and what it commits to. That is a different operating model, and it requires CX leaders to argue for a different seat at the table.

The promise is tested after the sale. The technology is finally good enough to honor that fact. The leaders who act on this stop investing in service as the place where the brand gets defended, and start investing in it as the place where the brand gets renewed every day the customer is still in the relationship. That is what comes after deflection, and it is the only frame that takes agentic AI seriously.

Customer service has historically been the place where the enterprise’s failures of imagination meet the customer. The product team didn’t think of that edge case. Pricing didn’t anticipate that scenario. Engineering shipped a feature that assumed the customer would think the way the org chart does. Service catches all of it, and the only reason that arrangement worked for decades is that humans inside service teams were absorbing the cost of routing context the rest of the enterprise couldn’t.

This cost is one that very few understood or priced. The reality was that customer service wasn’t a department, it was a load-bearing translation layer holding the rest of the business together. The activity got contained in a department because the technology of the time couldn’t route enterprise context any other way.

AI changes that constraint, and the strategic question for CX leaders shifts with it. The question isn’t how to deflect more contacts or close more tickets faster, it’s how to activate the entire enterprise on behalf of the customer, in real time, with the context required to actually solve the problem rather than simply deflect it.

Amdahl Comes for Value Delivery Too

In the previous piece I argued that Amdahl’s Law caps productivity gains: speed up 30% of a workflow and the other 70% defines your ceiling. The same law applies to value delivery, just with a different focus.

If your service organization can resolve a customer’s stated issue in two minutes but the underlying product gap takes six quarters to fix, your actual service excellence is bounded by your product cycle. If your agents can identify a billing pattern that predicts churn but finance can’t act on the signal for ninety days, your CX intelligence is bounded by your finance cadence. The constraint on continuous value delivery isn’t service capacity, it’s the enterprise’s capacity to absorb what service is learning.

This is why deflection metrics are a dangerous north star. They optimize a fragment of the true value journey of customers. Optimizing the part of the system that touches the customer, while the parts that produce durable value for the customer stay slow. This just produces a more efficient veneer over an unchanged business.

From Task Orientation to Process Orientation

The hard part of AI in CX isn’t getting it to handle tasks well, it’s getting it to step above tasks into the process layer where value actually moves.

Task-oriented AI answers the question, summarizes the ticket, drafts the response, and routes the case. It’s useful, and it’s where most deployments live today. Process-oriented AI does something different: it watches the patterns across thousands of customer interactions, identifies which of them encode signals that the rest of the enterprise needs, and routes those signals to the teams that can act on them. Product hears the friction pattern that’s driving 12% of contacts before it shows up in the next NPS survey. Finance sees the dispute trend before it becomes a quarterly variance. Engineering gets the product failure pattern before it lands on product review websites.

That’s the shift, and it’s harder than it sounds, because most enterprises aren’t structured to receive signals from service. Service was supposed to absorb the problem, not propagate it back upstream. Process-oriented AI breaks that thinking, and the redesign work is in deciding how the rest of the enterprise responds when it does.

The End of the Deflection Mindset

The deflection mindset treats every customer contact as a cost to minimize. The process mindset treats it as a context signal to absorb.

These two mindsets produce radically different economics over time. Deflection optimizes the existing product against the existing customer base. Learning compounds, because every interaction adds to the enterprise’s understanding of how customers actually use what they bought, what they were trying to accomplish, where the product met reality and where it didn’t.

That last piece of the puzzle is the prize. Most enterprises have detailed data on what customers buy and almost no data on the context in which they use it. AI agents that operate at the process layer, routing context across the enterprise rather than burning it inside service tickets, finally make that context legible at scale.

The CX organizations that internalize this won’t look like service organizations anymore. They’ll look like the nervous system of the enterprise, sensing where value is leaking and routing the response to wherever it actually has to come from.

That’s what the next phase looks like, and it starts the moment you stop focusing solely on deflection.

In the midst of all the AI hype, CX leaders are challenged to solve the same problem in two languages. The first language is quarterly: the board wants AI wins, velocity, cost reductions, and ROI to demonstrate to their shareholders. The second is structural, and it lands six months later when the same board asks why CSAT moved three points but revenue per agent didn’t, and why the P&L doesn’t reflect greener dashboards.

These languages aren’t contradictory, rather they’re sequential, and conflating them is what sits underneath every “AI is everywhere except in the bottom line” narrative that seems to be everywhere.

The Phase One Fixation

McKinsey’s recent piece on where AI will and won’t create value is, on a careful reading, a memo about timing. It names three waves: productivity gains, differentiation, and transaction-cost reduction. Most organizations are deep into the first wave and organizing as if it’s the only one that matters.

That’s the risk. McKinsey argues productivity gains rarely expand profit pools, because competition erodes them and customers capture most of the value (as it should be). Phase One is table stakes, not the prize. Real value comes later, when AI changes what you can offer customers and reshapes the economics of the markets you compete in. Treat Phase One as the destination and you’ll miss the transformation you think you’re executing.

Even Phase One Isn’t Compounding

The issue is that, in the real world, most organizations aren’t winning Phase One either. Goldman Sachs puts task-level savings around an hour a day, and Deloitte shows 66% of organizations reporting productivity gains while only 20% see revenue growth from them. As I wrote about previously, Amdahl’s Law, is in full swing with Phase One.

If AI accelerates 30% of a workflow, the improvement is capped by everything it doesn’t touch. Approval chains, handoffs, exception paths, and process structures built for constraints that no longer exist all stay in place, and speeding up part of a workflow that was never designed for speed just produces a faster version of the same output.

Stanford’s research puts numbers on this: companies that redesigned workflows around AI saw median productivity gains of 71%, while those that deployed the same AI without redesign saw 30%. The only variable was the redesign.

Beyond Phase One: Redesigning work

Stuart Winter-Tear’s recent Substack post has the cleanest framing for this angle. The moment AI touches live work, he writes, “it stops being ‘just software.’” It becomes behavior on the organization’s behalf, which means you’re changing how work moves, where judgment lives, and who absorbs the consequences when the workflow is wrong.

He names the unpriced cost as AI Translation Debt: the work of repairing meaning where work crosses teams, systems, vendors, or approvals. Humans used to absorb that debt invisibly, but AI doesn’t remove it. It collects it, amplifies it, and presents it back as “review.” That’s why CX teams feel busier rather than lighter after the first wave. The tools work, but the workflow was never redesigned to carry them.

Framing for the Next Phase

The CX leader’s job is to hold two horizons at once: short-term wins that prove value this quarter, and a redesign thesis that prepares the organization for the subsequent phases. Quick wins without a thesis don’t compound, and a thesis without quick wins doesn’t survive the budget cycle. There are essentially four moves to keep both clocks running in strategic alignment.

Pick workflows, not tasks. Choose end-to-end CX workflows where delegation could plausibly change unit economics: onboarding, escalation, retention. “Deflect this query” isn’t a workflow, it’s a task, and tasks alone don’t serve the long term time horizon.

Budget the intangibles. Stanford’s data is unambiguous: for every dollar of AI technology, organizations that achieve real productivity gains spend up to ten dollars on process redesign, reskilling, and change management. If your AI budget is mostly licenses, you’re underwriting a J-curve you won’t survive.

Make the seams visible before you scale. Where are humans currently filling in what the workflow doesn’t specify? Name it, price it, and decide whether that judgment stays human, gets delegated, or gets engineered out. Skip that step and Translation Debt scales invisibly, surfacing later as escalation load and margin leakage.

Anchor every quick win to a Phase Two thesis. Each deployment should answer one question: which moat is this building or eliminating? Proprietary data, workflow embedding that raises switching costs, faster learning cycles? A quick win that doesn’t compound into structural advantage is a productivity demo, not a strategy.

The CX leaders who define this era won’t be the ones who optimized hardest for Phase One, they’ll be the ones who used short-term wins to fund the longer redesign.

And what’s coming for CX aren’t faster sales, service, marketing departments, it’ll be the dissolution of the department line itself. For example, customer service and support were never genuinely a department, they were artifacts of a system that couldn’t route enterprise context fast enough to act otherwise. AI changes that constraint. The next phases of value will come from teams that step past “the job to be done” and design for the value to be provided to the customer, wherever in the enterprise that value lives.

More on this line of thinking next week.