Agents Are Not the Model
The current path on the road to agent differentiation
TL;DR:
Every serious agent runs the same 2–3 foundation models — the brain isn’t the differentiator
Differentiation moves up the stack: identity, data, tools, and instructions
Payments between agents already work — trust, identity, and discovery don’t
The layers where differentiation matters most have no one building them
Whoever builds those missing layers will define how agents compete, get found, and earn trust
Agents Are Not the Model
Your agent contacts five coffee agents. Four respond. One is a scam. Your agent can’t call the Better Business Bureau — it only sees structured data. Four agents claiming to sell coffee at $6.50, all formats identical, all polite, all convincing. The first real problem of the agent economy isn’t whether agents are smart enough. It’s whether they know who to trust.
The Stack
It's easy to conflate the model with the agent, but they're not the same thing. The model isn't exactly irrelevant — it's the foundation everything else depends on. But currently, differentiation does not happen on the frontier.
An agent is a stack. Five layers, bottom to top: model, instructions, tools, data, identity. The model — Claude, GPT, Gemini — sits at the bottom. It’s the brain. Everything above it is what makes an agent an actual entity in the world. The brain is what makes the entity capable. Both halves matter. The distinction is about where differentiation happens, not where competence lives.
Right now, agents use one of two or three commercial models. Claude, GPT, maybe Gemini. The frontier is narrow. If every serious agent runs roughly the same brain — and today, they do — then the brain can't be what separates them. Differentiation moves up the stack by necessity, not because the model doesn't matter.
This is counterintuitive if you’ve been following the model wars. Every week brings a new benchmark, a new leaderboard reshuffle, a new claim about who’s ahead. That conversation assumes the model is the product. For chatbots, maybe. For agents — entities that hold money, make commitments, represent businesses in binding transactions — the model is necessary but insufficient.
As a thought experiment, take those coffee shops.
Blue Bottle runs an agent. Claude powers it. Claude is what makes it capable of understanding your order, reasoning about substitutions, handling an edge case where your usual drink is out of stock. That matters. But Claude isn’t what makes it Blue Bottle. The identity layer carries a $50,000 credit line and 12,000 successful transactions. The data layer knows seasonal menus, real-time inventory per location, your taste profile from three years of orders. The tool layer connects to Square for payments, DoorDash for delivery, inventory management for restocking triggers. The instructions say: premium positioning, never discount, suggest food pairings, prioritize regulars.
Joe’s Deli, two blocks away, also runs Claude. Same brain. Same raw capability. Completely different entity. $500 prepaid balance on Venmo. A ten-item menu. No delivery — pickup only. The instructions say: compete on price, accept special requests, text the owner for anything over twenty bucks. Joe’s agent and Blue Bottle’s agent have the same model.
Then there’s Scam Coffee Co. Also Claude. Same brain again — and the brain is just as capable here. Disposable crypto wallet. A menu copied from Starbucks. No fulfillment capability. Instructions: undercut everyone by half, collect payment, vanish.
Same model powers all three. The model made all three competent. The model didn't differentiate them. The model didn't protect you from the scam. The brain is what makes all three of them capable of complex behavior — but what separates them is everything stacked on top. Training, tools, ethics, credentials, etc…
If the frontier stays narrow — two or three dominant models powering most agents — then model convergence pushes differentiation elsewhere by default. Up the stack, into identity, data, tools, context. The interesting question isn’t ”which brain is best.” The question is ”what’s wrapped around the brain, and who controls it.”
Payments and Trust
So how do agents pay each other? Today, payments for agentic commerce do exist. They even work. Take A2P, from Google or x402, from Coinbase — a direct stablecoin transfer, agent to agent, settling in seconds instead of the two days your credit card takes.
Trust is the unsolved part. And I want to be precise here — what I’m about to describe doesn’t exist yet. These are proposals, architectures being discussed, not infrastructure you can point at. The ideas are real. The implementations are not.
The proposal goes like this. Before your agent sends money to Blue Bottle’s agent, it queries a trust network: is this entity real? The network returns a score. Blue Bottle gets 0.92 — 847 successful deliveries, 12 attestations from agents you’ve transacted with before, no fraud reports. Scam Coffee Co gets 0.23 — new account, two disputed transactions, pattern match to known scams. Your agent picks Blue Bottle. Skips the scam. No human needed.
It sounds clean. It isn’t.
A trust score is really a centralized authority. Someone defines ”trustworthy.” Someone weights the variables. Someone decided that 847 deliveries plus 12 attestations equals 0.92. That’s not decentralized trust. That’s a credit bureau for agents. And credit bureaus have a track record: they centralize, become gatekeepers, encode biases into scores that look objective but aren’t. A brand-new agent with zero history gets a low score, because trust systems reward incumbents. The scam gets caught, yes. But so does every newcomer. Trust scores don’t just filter bad actors. They calcify existing power structures.
These problems aren’t hypothetical even though the systems are. We’ve seen every one of them play out in credit scoring, app store rankings, search indexing. The patterns are well-established. Building them again for agents doesn’t solve them.
The Honest State of the Stack
The identity layer has no powerful sponsor. Anthropic built MCP because they sell capabilities. Google built A2A because they enable communication. Each company built the layer it profits from. Identity systems are designed for 8 billion humans. How many agents will there be? If every API call can spawn an agent, you’re looking at trillions. You can’t issue verifiable credentials at that scale. You can’t maintain reputation graphs for trillions of entities with lifespans measured in milliseconds. That’s a problem to solve. However, the stack is being built by whoever has the most to gain from each layer — not by whoever is best positioned to solve each problem. Identity has no powerful sponsor, which is why it’s still philosophical. This isn’t an implementation gap. It’s a category error, compounded by the fact that nobody with money has a reason to fix it.
The discovery layer doesn’t exist. Your agent found five coffee shops. How? There’s no agent directory. No yellow pages. Capability protocols exist — MCP lets agents advertise what they can do. Communication protocols exist — A2A lets agents talk to each other. But nobody’s building the layer that lets agents find each other in the first place. The most economically valuable layer in the entire stack — whoever controls discovery controls the ecosystem. Google is the search company and discovery is their core competency. If they believed the open agent stack was real, maybe they’d have announced a discovery protocol. Search taught us that discovery is less of a neutral infrastructure and more of a chokepoint. Maybe, whoever builds this agent directory will own a good chunk of the economics of the entire system.
The Trust Problem Is a Power Problem
Every layer of this infrastructure — built or proposed — encodes someone’s judgment about what ”trustworthy” and ”discoverable” mean.
Trust scores might look like math, but they're politics. Identity systems look like infrastructure. They're actually governance. Discovery looks like search, when in fact it's market-making. Whoever builds each layer defines the rules. And rules, once laid, outlast the people who wrote them.
The coffee shop example makes it feel like a convenience story — your agent finds coffee faster. But the real question is: who decides which coffee shops your agent can find? Who defines the scoring function that filters out the scam — and also filters out the newcomer? Who builds the directory, and what does it cost to be listed?
The model matters here too. If three companies control the brains that power most agents, they’re not just model providers — they’re kingmakers. They decide which capabilities agents have access to, which safety constraints apply, which use cases are allowed. The brain isn’t neutral infrastructure any more than the trust layer is. The difference is that the brain already has powerful sponsors. Three of them. The question is whether the rest of the stack gets built by those same three companies — or by someone else.
On the other hand, if the frontier widens — if open-source models close the gap, if specialized models emerge for specific domains — then the model layer becomes a differentiation surface. Maybe that's the true goal?
Joaquin Perez writes Loopcraft, a newsletter about individual power in the age of AI. If this resonated, subscribe at loopcraft.io.