Agent observability and evaluation

Agents are hard to debug from the outside. A single request can trigger a dozen model calls, several tool calls and a few retries before an answer appears, and a wrong answer at the end can come from any of those steps. Observability tools record each run as a trace, a timeline of every step with its inputs, outputs, timing, tokens and cost, so you can see exactly where things went off course.

Evaluation tools answer a different question: is the agent getting better or worse? You build a set of test cases from real requests, decide what a good result looks like, and score each version of the agent against the set. Scores can come from code checks, from a model acting as a judge, or from people reviewing outputs. Running the same set before every change catches regressions that would otherwise reach users.

Most products here do both, with different emphasis. Some started as tracing tools and added evals. Others started with evals and experiments and added production monitoring. A few focus on guardrails, which check inputs and outputs in real time and block or fix ones that break a rule, such as leaking personal data or calling a tool with bad arguments.

For agents specifically, look for views that show tool calls clearly, including the arguments the model chose and what came back, and that group multi-step runs into one session. Metrics such as whether the right tool was picked and whether the task was completed matter more for agents than for single-answer chat apps.

Data handling deserves a hard look. Traces often contain customer messages, documents and tool outputs. Check where traces are stored, how long they are kept, whether fields can be masked before they leave your systems, and who on your team can read them. Open-source tools here can be self-hosted if traces must stay on your own infrastructure.