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Agent Evaluation Methods

Beginner explanation​

An eval is a repeatable test for AI behavior. It checks whether the system does what it should and avoids what it should not do.

Production explanation​

Production teams evaluate individual prompts, retrieval quality, tool use, workflow behavior, and safety boundaries. Good evals are tied to concrete product risks, not just general notions of answer quality.

Real-world enterprise example​

A support copilot is evaluated on escalation detection, refusal correctness, citation quality, and whether it avoids calling external tools when policy says it should not.

Mermaid diagram​

TypeScript example​

export interface EvalCase {
  id: string;
  input: string;
  expectedBehavior: string;
  forbiddenBehavior?: string;
  requiredSources?: string[];
}

Python example​

def passes_refusal_test(output: str, should_refuse: bool) -> bool:
    refused = "not enough reliable information" in output.lower()
    return refused == should_refuse

Common mistakes​

  • writing vague eval cases that cannot be scored consistently
  • testing only final answers and ignoring tool or retrieval behavior
  • not updating evals when product behavior changes
  • treating one successful manual demo as proof of quality

Mini exercise​

Create five eval cases for one project: one happy path, one ambiguous input, one tool failure, one refusal case, and one adversarial case.

Project assignment​

Build the first offline eval set for your orchestrator or RAG project.

Interview questions​

  • What is the difference between a regression test and an eval?
  • Which evals should run before every release?
  • How do you score behavior that has multiple acceptable outputs?

Monetization angle​

Evaluation design is valuable because most teams do not know how to operationalize AI quality. This is recurring work, not just one-time setup.