Agent Evaluation
grading. Gate
your agents.↘
What's the difference between agent observability and agent evaluation,
and which do most teams skip?
Observability is descriptive: logs and traces of every step, tool call, and token. It tells you what happened and is great for debugging.
Evaluation is the judgment layer on top: a repeatable way to score outcomes against what should have happened, as a pass or fail, a rubric, or a number.
Adoption is lopsided, roughly 89% observability vs 52% evaluation, so most teams can watch their agents but never actually test them. That's why so many agents feel fine in a demo and disappoint in the wild.
tool call, token
happened
what should happen
a number
still be wrong?”
→ Yes. A trace records what
happened, it never says
whether it was right.
How does trajectory evaluation score an agent, and what does it
look at beyond the final answer?
Agents are multi-step, so a single "was the final answer good?" check is too coarse. Trajectory evaluation scores the path: did it choose the right tool, retrieve the relevant document, avoid needless steps, and recover when something failed?
The point is that two runs can land on the same answer, one through sound reasoning and one by luck, and only trajectory-level evals can tell them apart. That distinction is exactly what you need before trusting an agent with real actions.
Why does a dashboard that logs every step still leave you exposed,
and what does an eval gate catch that logging can't?
A trace shows the agent called three tools and returned an answer. It does not tell you whether that was the right answer, the right tools, or the cheapest path.
It surfaces a failure only after it ships, when an angry user hits it.
An eval gate runs the change against a golden set in CI and fails the build if scores drop below baseline.
production.
The gate stops it
before it ships.
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