What signals matter most when evaluating tool-using agents?

Instruction: Explain what you would look at beyond the final answer when evaluating an agent with tools.

Context: Tool-agent correctness, authorization, and recovery.

Updated

Example Answer

For tool-using agents, I care about more than whether the final answer looked right. I want to know whether the agent chose the right tool, used the right arguments, respected preconditions, recovered from failures sensibly, and stopped when confidence was too low to proceed safely.

So I usually look at step-level correctness, tool-call validity, unnecessary tool usage, latency across the workflow, error recovery behavior, approval handling, and end-to-end task completion. The right tool decision at the wrong time can still break the user experience.

I also separate visible success from hidden risk. An agent can get the answer right once while taking a path that is expensive, brittle, or unsafe. If I only score the surface output, I reward behavior that will hurt me later in production.

Concrete example

In a ticket-routing task, check the destination queue, required fields, authorization, actual ticket creation, and final acknowledgment. Include a timeout after the tool may already have created the ticket.

Limit to explain

Tool success is not task success; valid arguments can still target the wrong account. Judge the resulting state as well as the call.

Follow-up to practice

How would you detect duplicate side effects after a retry?

Your preparation path

Work through these questions in order. Read the answer aloud, then explain it in your own words.

1. Start with the foundations

Build the vocabulary and explain the core decisions.

  1. What problem should offline evals solve before you ship an AI feature? Free sample
  2. How would you explain the difference between task success and model quality? Free sample
  3. What makes a good regression set for an AI workflow? Free sample

2. Apply it to a real workflow

Practice diagnosis, validation, and everyday tradeoffs.

  1. What signals matter most when evaluating tool-using agents? Free sample
  2. Your tool-using agent passes answer-level evals and still makes bad tool calls. How would you fix the blind spot? Member answer
  3. Production feedback says answers feel less trustworthy even though offline metrics are flat. How would you investigate? Member answer

3. Prepare for senior discussions

Explain failure boundaries, recovery, and production choices.

  1. Human reviewers are expensive, but model graders miss subtle safety issues. How would you balance the two? Member answer
  2. Two coding agents have different scores and sandbox limits. How would you compare them fairly? Member answer
  3. An answer tells your LLM grader to award full marks. How would you keep the evaluation trustworthy? Member answer

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