AI agent evals are the structured tests that define what « correct » looks like for an automated task, and without them, an AI agent workforce degrades into expensive, self-repeating loops instead of finished work.
Gartner projects that 40%+ of agentic AI projects will be canceled by 2027 over unclear ROI and runaway cost, and evaluation gaps are the single largest reason pilots never reach production. Most companies bought agents. Almost none built the scoreboard that tells the agents, and the humans managing them, what winning looks like.
The contrarian part: giving every employee an AI agent was never the productivity unlock it was sold as. It was a headcount grant. Unlimited, unsupervised, and running 24 hours a day.
Warcraft Rules Apply Here
In Warcraft, you don’t win by rushing units at the enemy base. You win with build order: peons before barracks, barracks before army, army before attack.
Skip a step and you get overrun by your own unmanaged expansion. Most companies deploying AI agents in 2026 skipped straight to the attack. They handed out Claude Code, Copilot, and custom harnesses to a workforce that had never been taught to define a task clearly, then wondered why the agents multiplied sub-agents instead of finishing anything.
Build order isn’t a metaphor but the missing operating discipline, and evals are the peons nobody built first.
Tokenmaxxing Was Never About Tokens
The token-spending spree of early 2026 solved nothing, because token volume was never the constraint.
A16z’s George Sivulka put it plainly in his July 2026 essay on the topic: « people are spending so much on tokens because they don’t know how to use them. » Roughly one in a hundred employees can articulate a process clearly enough for an agent to execute it without supervision. Everyone else generates loops: agents calling themselves to fix themselves, because the original instruction was never precise enough to succeed on the first pass.
Loops are meetings about meetings, restaged as compute. The bill looks like an infrastructure problem.
It is a management problem wearing a GPU invoice.
Forrester’s 2026 study of 287 enterprise agent deployments found average ROI of 540% within 18 months for the deployments that made it to production, with a 7.3-month payback period.
The catch: only 41% of agent rollouts hit positive ROI within 12 months at all. The gap between those two numbers is entirely explained by whether the company built evaluation infrastructure before it built agent count.
Eighty Percent of Your Tokens Are Doing Nothing
Just as eighty percent of employees in a mismanaged company don’t meaningfully move the business, eighty percent of tokens spent today produce nothing usable. They loop, retry, and stamp approvals in a machine that exists to keep existing.
It’s the same one that hit American railroads in the 1830s. Track mileage grew 120x in a decade with no coordination layer underneath it, and on October 5, 1841, two trains collided head-on in Massachusetts because nobody had defined who was responsible for what. The fix wasn’t more track. It was modern management: written roles, clear reporting lines, defined accountability. Rail then became one of the first trillion-dollar industries, at its peak representing roughly 60% of the entire stock market.
AI agents are the same railroad, a century and a half later. The infrastructure is fast, cheap, and completely undisciplined. Someone still has to build the timetable.
Evals Are the New OKRs, and Almost Nobody Has Them
The one AI use case that escaped internal politics and delivered consistent value is coding, and It’s because code has a built-in eval: it runs, or it doesn’t. Ninety-nine percent of realized AI revenue today concentrates in coding for exactly this reason.
Every other business function is stalled precisely where coding wasn’t: nobody has translated « good customer service, » « good financial forecast, » or « good client onboarding » into something an agent can be scored against. Recent data on pilot failure confirms this at scale. 64% of enterprise leaders cite evaluation gaps as a top blocker to moving agents from pilot to production, ahead of governance friction (57%) and raw model reliability (51%).
A firm’s eval suite, not its model subscription, is becoming its most defensible asset. Generic evals produce generic agents. Nobody builds competitive advantage on a shared benchmark.
Nobody Trains Their Replacement for Free
There is a second reason evals stay unbuilt, POLITICS.
Building an eval means codifying exactly how your best employee does their job, in enough detail that a machine can execute it without them. Tribal knowledge has been job security since medieval guilds kept their methods secret. AI is the first technology that asks an employee to hand all of it over in one sitting. Even at Meta, a company staffed almost entirely by people financially incentivized to make AI work, employees revolted over their own work being used as training data.
The people holding the « 100x tokens, » the ones who know how to get an agent to actually produce something, have the least personal incentive to formalize that knowledge into an eval anyone else could run. Left to internal politics, this problem never resolves itself. It has to be run as a structured, external process, or it simply doesn’t happen.
AI Agent Evals vs. Token Volume: What Actually Predicts Success
| Signal | What it measures | Correlation with production success |
|---|---|---|
| Token spend per employee | Volume of AI usage | Weak — high spend often means more loops, not more output |
| Headcount using AI tools | Adoption breadth | Weak — adoption without process yields inconsistent results |
| Evaluation coverage | % of workflows with a defined « correct » | Strong — cited by 64% of leaders as the top blocker when missing |
| Governance and ownership model | Clear accountability for agent output | Strong — 36% of firms have no formal supervision plan and stall accordingly |
| External eval-building process | Whether evals are built by someone with no stake in hoarding knowledge | Strong — bypasses the internal incentive to protect tribal knowledge |
Token volume and tool adoption are what companies measure. Evaluation coverage and ownership are what actually predicts whether an agent deployment survives past the pilot.
Why This Is a Transformation Problem, Not a Tooling Problem
Silicon Valley’s current bet is that « neofirms, » AI-native services startups, will eat the $21 trillion knowledge-services economy because incumbents are too politically mired to manage their own transition. That bet misses where the real asset sits. The differentiated process, the actual know-how that makes a firm’s output better than a competitor’s, already lives inside the incumbent. It just isn’t written down anywhere an agent can read it.
Palantir is the clearest proof this works at scale. On paper, it should be the most disruptible company in enterprise software, hand-building bespoke applications in an era when foundation models are supposed to make bespoke software obsolete. It isn’t disrupted, because Palantir was never selling software. It was selling the transformation: understanding a business deeply enough to encode it. That is the same work evals do, at a smaller and faster scale, for a single function instead of an entire enterprise stack.
Encoding a firm’s specific way of doing things into agents, then proving it with evals instead of hoping the model behaves, is becoming the largest addressable task of the next decade. Most companies won’t build this capability in-house. They don’t have the spare 1-in-100 employee who can write the evals, and the ones who could have no incentive to.
This is exactly the gap Asymmetriq is built to close.
Asymmetriq runs as a managed function, not a tool subscription. Instead of handing a team another chat interface and hoping the 99 out of 100 who can’t prompt clearly figure it out, Asymmetriq’s team sits with the process that already works inside the client’s business, converts it into evals, and builds the agent against that scoreboard, not against a generic benchmark. The output is a function that passes a defined bar, monitored on a recurring basis, the same way a firm would manage a hire rather than a software license.
That distinction is the entire argument of this piece, operationalized. Evals beat token volume. Process encoding beats generic tooling. And an external team, with no incentive to protect tribal knowledge for its own job security, is structurally better positioned to build the eval suite than the internal employee who would be handing over their own leverage for free.
FAQ
Q: What are AI agent evals, exactly? A: Evals are structured, repeatable tests that define what a correct output looks like for a specific task, the same way unit tests define correct behavior for code. Without them, an AI agent has no objective way to know if its output is right, so it defaults to retrying, second-guessing, and looping.
Q: Why do 89% of AI agent pilots fail to reach production? A: Because most pilots start with tooling, not measurement. Data from 2026 pilot studies puts evaluation gaps (64% of leaders), governance friction (57%), and model reliability (51%) as the top blockers, in that order. The technology usually works. The scoring system around it doesn’t exist.
Q: Is buying more AI agent tools actually worth it without evals? A: No, and the ROI data backs this up. Deployments that reach production average 540% ROI within 18 months, but only 41% get there within a year. The difference between those two numbers is almost entirely evaluation and governance maturity, not model quality.
Q: Why do most companies get AI agent management wrong? A: They treat agents like software, deployed once and left running, instead of like a workforce that needs defined roles, evaluation, and ongoing management. Tokens don’t quit, but they also don’t improve on their own between sessions. Someone still has to manage them.
Q: Can employees build their own evals instead of outsourcing it? A: In theory, yes. In practice, the employees who know a process well enough to encode it are also the ones with the least incentive to formalize their own replacement. This is a structural, political problem, not a skills gap, which is why external, incentive-neutral teams tend to get it built faster.
Q: What’s the actual difference between a « neofirm » and a transformation company like Asymmetriq? A: Neofirms bet that incumbents can’t manage their own AI transition and try to replace them outright. A transformation approach bets the opposite: that the differentiated process already sitting inside the incumbent is the real asset, and the job is encoding it into evals and agents rather than building a competitor from scratch.
The Verdict
Humans became cheaper than software for the first time in history, and almost nobody noticed the actual headline: someone still has to manage both. The companies burning budget on token volume are optimizing the wrong variable. The ones building evaluation infrastructure before scaling agent count are the only ones clearing production. Asymmetriq exists because that infrastructure doesn’t build itself, and the people best positioned to build it internally are the least incentivized to. If your AI agents are looping instead of shipping, the fix is a scoreboard. Talk to Asymmetriq about building yours.