Agent Evaluation Frameworks: Measuring What Matters

You cannot improve what you cannot measure. Except with AI agents, most people are measuring the wrong things.

"Our agent has 97% accuracy!" Great. But accuracy on what? Measured how? Against what baseline? With what edge cases excluded? The number means nothing without context. And most evaluation frameworks provide exactly that. Numbers without context.

Agent quality is not one number. It is a web of tradeoffs. Your agent might complete tasks reliably but take too long. It might respond quickly but give shallow answers. It might handle common cases perfectly and hallucinate wildly on edge cases. A single score hides all of this.

So stop looking for one number. Build a framework that captures the dimensions that actually matter for your use case.

Task Completion Is Table Stakes

Yes, you need to track whether the agent completes tasks. But the raw completion rate is the least interesting metric once you get past 80%.

What matters more is how the agent completes tasks. How many steps did it take? How many tool calls? How much did it cost in API tokens? Did it need to retry? Did it ask the user for clarification it should have figured out on its own?

I track what I call "completion efficiency." An agent that completes a task in 3 steps and 500 tokens is better than one that completes the same task in 12 steps and 4,000 tokens. Both show 100% completion. Only one is actually good.

The most revealing metric is "completion with human intervention." Track every time a user has to correct, redirect, or help the agent. A 95% autonomous completion rate with 5% requiring a quick nudge is excellent. A 99% completion rate where 30% required the user to basically do the work themselves is garbage dressed up as a good number.

Reasoning Quality Is Everything

Here is what keeps me up at night. An agent that gives the right answer for the wrong reason.

It happens more than you think. The agent produces correct output, your evaluation marks it as a pass, and everyone moves on. But the reasoning was flawed. It got lucky. It pattern-matched to something in its training data that happened to be correct for this specific case.

Next week, a slightly different case comes in. Same flawed reasoning. Wrong answer this time. And you are scrambling to figure out what changed when nothing changed. The reasoning was always broken. You just were not checking.

Chain-of-thought evaluation is how you catch this. Do not just look at the final output. Look at the steps. Does each step logically follow from the previous one? Are the assumptions valid? Is the agent using the right information to reach its conclusions?

This is harder to automate. It requires human review or another AI model evaluating the reasoning. But it is the difference between an agent you can trust and one that is a ticking time bomb.

Baselines Give You Perspective

Comparing your agent to itself over time tells you if it is getting better. Comparing it to alternatives tells you if it is any good.

Every agent evaluation should include at least three baselines. First, a simple rule-based system. If your fancy AI agent barely outperforms a series of if-else statements, you have an expensive solution to a simple problem. Second, the previous version of your agent. This catches regressions. Third, human performance on the same tasks. This gives you a ceiling to aim for and context for your metrics.

I worked with a team that was celebrating their agent's 85% accuracy on customer support tickets. Impressive sounding. Until we measured human accuracy on the same tickets. Also 85%. The agent was not better than humans. But it was faster and cheaper. That is still valuable. But the story you tell internally and to clients is completely different.

Build Evaluation Into Your Pipeline

The biggest mistake is treating evaluation as a separate activity. Something you do quarterly. An afterthought.

Evaluation should run continuously. Every agent interaction should be scored. Daily dashboards should show trends. Alerts should fire when metrics drop below thresholds.

Build a test suite of 50-100 representative tasks across your agent's capabilities. Run it after every prompt change, every tool update, every model upgrade. Automated. No excuses.

The teams that win at AI agents are not the ones with the best models. They are the ones with the best feedback loops. They see problems first. They fix them first. They improve fastest.

User Satisfaction Is the Final Boss

All those technical metrics matter. But the metric that pays the bills is whether users actually like using your agent.

Track user satisfaction directly. Post-interaction surveys. Thumbs up/down on responses. Session abandonment rates. Repeat usage patterns. An agent with 90% task completion but 60% user satisfaction has a UX problem, not an AI problem. Maybe it is too slow. Maybe it is too verbose. Maybe it solves the task correctly but makes the user feel stupid in the process.

Satisfaction data also reveals gaps that technical metrics miss entirely. Users might be satisfied with a partially completed task if the agent communicated clearly about what it could and could not do. They might be dissatisfied with a fully completed task if the agent took a confusing path to get there.

Evaluation is not a report card. It is a steering wheel.