Below are five ways to observe and evaluate an AI agent.
Agent audit
Capture every step the agent takes (every prompt, tool call, output, and decision branch) with enough fidelity to replay or reason about the run after the fact. The audit log is to an agent what tracing is to a microservice.
When something goes wrong in production, the first question is what did the agent do? Without an audit trail, the answer is a guess.
Agent evaluation
Repeatable test cases that exercise the agent end-to-end against known inputs and expected outcomes. Eval suites run on every prompt change, model upgrade, or shift in the underlying context. They show whether the system improved or regressed.
Agent verification
The ability to confirm that the agent's output is correct, not just plausible. For an agent generating SQL, run the query against a sandbox and check the result. For one writing code, run the test suite. For one drafting a contract, review the draft against the required clauses.
Agent efficacy
A comparison against a baseline: human work, the previous version of the agent, or a simpler alternative. Without efficacy measurement, the question that funds the project goes unanswered: is this agent better or worse than the previous approach? In a customer chat application, escalation rate or customer outcome is the efficacy metric.
Agent budget
Track LLM token usage, tool-call counts, latency, and dollar cost. Then enforce limits. A per-run cap, a per-tenant quota, and a circuit breaker on cost are basic hygiene, not premature optimization.