Key takeaways
- A demo proves the vendor chose good examples. A bake-off proves the agent can do your work — on your cases, against your baseline.
- 48 hours is enough if you prepare 20–50 sanitized cases (normal path plus every exception type you know) and define “done” before the run starts.
- Score four things per case: correctness, evidence quality, exception behavior, and cost. Then time the human review of the agent’s output.
- The bake-off also tests governance for free: an agent that cannot show its evidence, attribute its actions, or be stopped cleanly fails before accuracy even matters.
Why demos mislead
Every vendor demo has the same property: someone selected the examples. The cases are representative enough to feel real and clean enough to go well. That is not dishonesty — it is what a demo is. But your decision is not “can this agent do a well-chosen case.” It is “will this agent do my cases, including the ugly ones, at a quality my team can verify, at a cost my CFO can see.” Those questions need your data, your exceptions, and your reviewers. A bake-off is the smallest honest test of that.
Before the run: the four preparations
1. Pick the cases. Twenty to fifty real cases from the workflow you actually want to transform — sanitized of customer-identifying data, but keeping the shape of the mess: the missing documents, the conflicting values between systems, the edge-of-policy judgment calls. Include every exception type the team already knows by name. The exception cases decide the evaluation; the happy path flatters everyone.
2. Define “done” before anyone runs anything. For each case, what is the correct outcome — or, where the outcome is judgment, who is the expert whose decision is the reference? Write it down. The most common bake-off failure is discovering afterward that nobody can say whether the agent was right.
3. Fix the evaluation interface. The agent should run the way it would run in production: its own identity, scoped access, a policy gateway, and an evidence trail. If a vendor can only run their agent with a developer’s credentials and no record, that is itself a result — it predicts exactly the governance gap that kills projects at the production review.
4. Instrument the baseline. For the same cases, how long does the current human process take, what does it cost, and where do the errors land? Without the baseline, “the agent did well” has no denominator.
During the run: what to score
Per case, four measures:
- Correctness. Against the defined outcome or the expert reference. Record not just right/wrong but wrong how — misread document, wrong rule applied, hallucinated a value — because the error taxonomy is what tells you whether the failure mode is fixable.
- Evidence quality. Did the agent cite the documents and fields it relied on, in a form a reviewer can check in minutes? This is the measure that predicts production viability: unverifiable output creates review burden that cancels the time saved.
- Exception behavior. On the ugly cases, did the agent flag uncertainty and route to a human, or guess with confidence? Confidence without calibration is the most expensive failure mode an agent has.
- Cost. The run’s actual cost per case — model calls, tool usage, and the human review minutes the output required. Cost per verified case is the number that matters.
And one measure across the run: can you stop it? Trigger the stop control mid-run. What happened to in-flight work? How long to produce the full list of what it did? This takes five minutes and answers the question your risk officer will ask months earlier than you expect.
After the run: the decision meeting
Put the same four numbers side by side for every agent evaluated, plus the human baseline. The comparison that usually decides the bake-off is not agent-versus-agent — it is agent-plus-review versus human-alone: total time per case including the human verification of the agent’s work, and quality at that combined time. An agent that is 90% accurate but doubles review time loses to one that is 85% accurate and reviewable at a glance. Then decide the next step on the evidence: a scoped production validation on the same cases, with the governance path included — or a stop, with the reasons documented.
What the bake-off deliberately does not do: touch your production systems, require data egress, or commit you to anything. It runs on sanitized cases, in a bounded window, and produces a decision artifact — the scores, the error taxonomy, the exception behavior — that doubles as the baseline for the production validation if you proceed.
The Moring version
We run this as a standing offer: a 48-hour bake-off on one of your workflows, your cases, our agents — governed end to end, evidence included, and you keep the evaluation artifact regardless of the outcome. It is also, deliberately, a test of us: an agent we cannot stop, attribute or evidence on day one would fail our own drill. If you want to see the governed-agent pattern on real cases before committing a workshop or a validation, talk to us — or start with the workflow walkthroughs to see the pattern on standard cases first.
Frequently asked questions
What is an AI agent bake-off?
A structured evaluation where one or more agents run against a fixed set of your real (sanitized) cases for a bounded time — 48 hours is enough — and are scored on accuracy, evidence quality, handling of exceptions, and cost per case. It replaces the demo, which proves the vendor chose good examples, with a measurement on your workload.
How many cases do you need for a 48-hour agent evaluation?
Twenty to fifty cases covering the normal path and the known exception types. The exceptions matter more than the happy path: most agents look identical on clean cases and diverge sharply on the messy ones — missing documents, conflicting values, edge-of-policy situations.
What should you measure in an agent bake-off?
Four things per case: was the answer correct (against a known outcome or expert review); did the agent cite the evidence it used; what happened on the exception cases (flagged, not guessed); and what the run cost. Also time the human review of the agent's work — that number decides whether the agent actually saves time.
Governing Autonomous AI Agents in Production
Moring provides the runtime control layer, identity verification, human-in-the-loop approvals, and audit records needed to deploy production-grade AI agents safely.
About the author
Balaji Nagaraj KumarChief AI Officer, Moring AI, Moring AISweden
Leads platform and delivery engineering at Moring AI, turning each engagement into a substrate the customer’s own team operates at handoff. His background spans developer-platform leadership at Volvo Cars and engineering at CERN. He writes about governance for agents that take actions in production systems.
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