// 2026-04-08 · 7 min read
Agent evals in practice
A pragmatic way to test tool use, retrieval and failure handling before production.
// The short version
A decision framework you can use
Good engineering writing should help a team make its next decision. The ideas below are intentionally practical: start from constraints, make trade-offs explicit and leave a system that can be observed.
// In practice
The details that matter
01
Start with representative tasks
A useful eval set looks like the work your users actually do: happy paths, ambiguous requests, missing context and requests that should be refused. Keep examples versioned so quality changes are visible.
02
Measure the whole loop
Evaluate retrieval, tool selection, arguments, final answers and handoffs separately. A fluent answer can still be wrong if the agent chose the wrong source or skipped a required approval.
03
Ship with a quality budget
Pair quality thresholds with latency and cost budgets. When a change improves one dimension but damages another, the trade-off becomes an engineering decision instead of a vague feeling.
Keep building