What is Agent Evaluation?
Agent evaluation is the process of measuring AI agent performance across dimensions like task completion accuracy, efficiency, safety, cost, and reliability — using benchmarks, test suites, and production metrics.
WHY IT MATTERS
You can't improve what you can't measure. Agent evaluation is harder than LLM evaluation because agents take actions with real-world consequences. It's not enough that the agent generates correct text — it needs to call the right tools, in the right order, with the right parameters.
Evaluation approaches include offline benchmarks (predefined test cases with known correct answers), simulation (agents operating in realistic but sandboxed environments), A/B testing (comparing agent versions on live traffic), and production monitoring (tracking real-world performance metrics).
For financial agents, evaluation must include financial accuracy (did it execute the right transactions?), policy compliance (did it stay within limits?), cost efficiency (gas costs, slippage), and adversarial robustness (does it resist prompt injection that could drain funds?).
HOW POLICYLAYER USES THIS
PolicyLayer's audit logs provide rich data for evaluating agent financial behavior — transaction volumes, policy compliance rates, spending patterns, and violation frequencies. This data helps assess whether agents are operating safely and efficiently.