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Evaluation of agents

**Evaluating agents** means checking whether a system that plans, uses tools, and takes multi-step actions actually finishes the job—safely, reliably, and at acceptable cost.

What it is

Evaluating agents means checking whether a system that plans, uses tools, and takes multi-step actions actually finishes the job—safely, reliably, and at acceptable cost.

Why it matters

Chatbots can be judged on answer quality. Agents can spend money, send messages, or change data. “Sounds smart” is not enough.

How it works (plain)

Good agent evals measure:

  • Task success: Did it finish the goal?
  • Trajectory quality: Sensible steps vs thrashing
  • Safety: Refused or escalated risky actions?
  • Cost/latency: Tokens, tool calls, time
  • Groundedness: Claims match tool outputs

Use fixed test tasks (like a quiz bank) and replay logs from real use—with privacy care.

Everyday example

An “inbox agent” that drafts replies: score on whether drafts match your style *and* never invent meeting times that were not in the calendar tool.

Try it

Write 5 pass/fail tests for one agent idea you care about. Include one “should refuse” case.

Myths

⚠️ Myth: If demos work, production will.
✓ Reality: Demos hide edge cases and adversarial inputs.
⚠️ Myth: Human vibe checks replace metrics.
✓ Reality: You need both—metrics catch drift; humans catch weirdness metrics miss.

Sources