Observability and evaluation answer different questions. Observability records what happened in a running system. Evaluation tests whether a defined expectation was met on a selected case or sample.
Both are useful. Neither removes the need to inspect what an agent did and decide what should change.
What observability gives you
Execution traces connect model calls, tool calls, timing, errors, and other recorded events. They help reconstruct how an interaction unfolded.
For example, a trace might show that a policy lookup failed and the agent still promised a refund. That is a concrete path worth investigating. The trace alone does not define the correct refund policy, establish why the model chose its answer, or show how often the failure occurs outside the observed sample.
Start with a bounded collection of relevant traces. Keep source relationships intact, handle private data deliberately, and record what is missing. Agentagon’s trace guide describes supported connections and imports; the privacy guide explains where data and model processing belong.
What evaluation gives you
An evaluation applies checks, rubrics, judges, or metrics to declared cases. It gives you a repeatable way to test an expectation and compare changes.
For the refund agent, the expectation might be: do not promise eligibility when the policy lookup fails. An evaluation can exercise that path alongside ordinary requests, and compare a baseline with a candidate that improves error handling.
The result is bounded by the cases, checks, and execution conditions. A passing score does not establish coverage of every production situation. A model judge’s output also deserves scrutiny; it is an assessment, not automatic ground truth.
Connect observation to a testable change
A practical loop has four steps:
- Investigate a concern. Inspect source and relevant traces. Save findings with enough evidence to understand the failure.
- Define the expectation. Turn the concern into cases and explicit checks, including behavior that should remain unchanged.
- Measure a candidate. Compare it against the baseline using the same frozen benchmark.
- Review the result. Inspect measurements, failures, coverage gaps, and the actual patch before deciding what to deliver.
Agentagon supports this loop through audits, evaluations, and measured fixes. Your coding agent supplies the reasoning and candidate edits. The CLI captures evidence, runs declared checks, and retains the results.
You can begin before connecting traces
A first code-only audit can identify a missing check or suspicious error path without a production observability connection. It cannot prove that the suspected behavior occurred at runtime. Keep that distinction visible in the finding.
When traces are available, use them to refine the concern and choose representative regression cases. When an evaluation reveals a failure, return to the evidence to understand it rather than treating the aggregate result as the whole explanation.
You do not have to start with a complete measurement system. Start with one question, one bounded set of evidence, and a comparison you can inspect.