A practical AI agent evaluation framework measures four things — task success, tool-call fidelity, cost, and latency — against representative real data, run by a repeatable harness so the result can be re-checked on every change. Its purpose is to replace “the agent seems to work” with a number you can defend, and to keep that number honest as the agent evolves.
Most agents that fail in production did not fail suddenly. They were shipped on the strength of a few good demo runs, with no agreed definition of “good enough” and no way to tell when they slipped below it. A framework is the discipline that closes that gap, and it rests on one rule: an agent should ship measured, not asserted.
What to measure
A useful framework weighs four signals together, because optimising one in isolation is how agents go wrong.
Task success. Score the agent against the outcome you actually care about, defined up front. For some tasks there is a known correct answer to check against. For many — research, drafting, classification on open-ended inputs — there is not, so you score against checkable properties instead: did it cite its sources, did the output match the required structure, did it stay within the rules it was given? The point is to define success before you measure it, not to rationalise whatever the agent produced.
Tool-call fidelity. Agents do real work by calling real systems, so a framework has to measure whether those calls hold up: the right tool, with valid inputs, returning valid outputs. A typed tool layer makes this checkable — you can score the rate of malformed calls and rejected inputs directly. Poor tool-call fidelity is one of the quietest reasons a demo collapses on real data.
Cost. Cost per task is a first-class evaluation metric, not an afterthought. An agent that is accurate but expensive to run may not be worth deploying, and one whose cost creeps up over time is regressing even if its answers look fine. Measuring cost in the harness is what makes it possible to engineer it down deliberately.
Latency. Measure how long the agent takes per task and per step. A correct answer that arrives too late fails the workflow it was meant to serve. Step-level latency also tells you where time is going when you need to make the agent faster.
How a harness works
A harness is the repeatable machinery around those metrics. The pattern is consistent regardless of domain:
- Assemble a case set from real, representative data — the inputs the agent will actually meet, including the awkward ones. Toy data produces toy confidence.
- Run the agent automatically over every case, capturing the full trace of each run, not just the final output.
- Score each run against your metrics — exact checks where a correct answer exists, property checks where it does not (citation present, structure valid, policy respected, tool calls validated).
- Compare to a baseline. A single score means little; a score against the last known-good run tells you whether a change helped or hurt.
- Re-run on every change. Because the harness is repeatable, you run it whenever the prompt, model, retrieval, or tools change — catching regressions before users do.
The repeatability is the whole value. A one-off evaluation tells you the agent was acceptable on the day you checked. A harness tells you it is still acceptable today, after the change you just made.
What this looks like in production
Agent Foundry Labs builds evaluation in as one of the composable layers every agent runs on — a built-in eval harness rather than a separate testing exercise. You can see the principle at work in our in-house outreach engine, a compliance-first research agent we built and run ourselves. From a single declarative profile definition it researched and fully drafted a batch of leads under hard compliance rules enforced on every message — and because the agent was measured and traced rather than assumed, we were able to engineer its running cost down materially while keeping account risk at zero. You cannot reduce a cost, or trust a compliance rule, that you are not measuring.
Evaluation sets the bar an agent has to clear; observability then watches a live agent against that bar in production. Together they are what separate an agent that survives daily use from one that only survived its demo.
If you want to know whether an agent in your business would clear a real bar — and what that bar should even be — that is exactly the conversation we start with. Book a 30-minute call and we can scope the measure first.