Evaluation isn’t about proving your agent is perfect. It’s about catching regressions and measuring tradeoffs. If you changed a prompt, a model, or a tool, what got better and what got worse?
Why most eval suites fail
The most common evaluation mistake is building a massive test suite, running it once, feeling good about the results, and never running it again. Large suites are slow, expensive, and hard to maintain. When they break, nobody fixes them because the effort outweighs the perceived value.
The second most common mistake is evaluating the wrong things. Checking whether the agent’s output is grammatically correct tells you nothing about whether it solved the user’s problem.
Start with real user queries
Start by collecting real user queries. Not synthetic ones you made up at your desk, actual queries from production or beta users. These capture the ambiguity, typos, and edge cases that synthetic data never does.
Then label outcomes that matter: correctness (did the agent complete the task?), safety (did it stay within bounds?), latency (was it fast enough?), cost (how many tokens did it use?), and user satisfaction (did the user get what they needed?).
The 30-test-case rule
A small suite that runs on every deploy beats a huge suite nobody trusts. Pick 30 test cases that cover your most important scenarios: 10 happy-path cases for the most common user intents, 10 edge cases that have caused problems before, 5 safety-critical cases that must never regress, and 5 performance-sensitive cases where latency or cost matters.
Run these on every code change. If a deploy breaks any of them, block the release. This is your regression safety net.
Measuring tradeoffs, not perfection
Agent development involves constant tradeoffs. A more detailed system prompt might improve correctness but increase latency. A cheaper model might reduce cost but increase error rates. A stricter safety policy might prevent harm but also block legitimate requests.
Good evaluation surfaces these tradeoffs explicitly. After every change, compare the metrics side by side: correctness went up by 5%, but latency increased by 200ms. Now you have a real decision to make, not a guess.
Automated vs. human evaluation
Some things can be evaluated automatically: did the agent call the right tool? Did it stay under the latency budget? Did it trigger any safety violations? Sentinel logs every violation, making automated safety evaluation straightforward.
Other things require human judgment: was the response helpful? Was the tone appropriate? Did the agent miss context that a human would have caught? For these, build a lightweight review process where a team member reviews a random sample of interactions each week.
Continuous evaluation in production
Evaluation doesn’t stop at deployment. Production traffic reveals failure modes that no test suite can anticipate. Use Helix observability to track success rates, latency percentiles, and cost per interaction in real time. When a metric dips, investigate immediately rather than waiting for a user to complain.
The goal is confidence, not paperwork. If your eval process feels like bureaucracy, simplify it until it feels like a tool.