AI agent evaluation is the practice of measuring whether an agent does its job correctly, safely and efficiently: before you release it, every time you change it, and while it runs in production. It is harder than testing normal software, because the same input can produce different outputs and because an agent makes a chain of decisions, not one.
Without evaluation, every prompt change is a guess. With it, you know whether version two is better than version one before customers find out.
What to evaluate
- Task success: did the agent reach the right outcome? Was the refund issued correctly, the ticket routed to the right team?
- Tool use: did it call the right tools, with the right arguments, in a sensible order?
- Output quality: are replies accurate, complete and in the right tone?
- Safety and policy: did it stay within its permissions, avoid restricted data and ask for approval when it should?
- Efficiency: how many steps, tokens and seconds did it take, and what did it cost?
- Robustness: does it behave when input is messy, ambiguous or adversarial?
How to build an evaluation set
- Collect real cases. Pull 50 to 200 examples from real tickets, requests or transactions, including awkward ones.
- Write the expected result. For each case, record the correct outcome, the tools that should be called and any action that must not happen.
- Add edge cases. Missing data, conflicting instructions, requests outside the agent's scope, prompt injection attempts.
- Keep it current. Every production failure becomes a new test case.
How to score
Code-based checks
Deterministic checks for anything with a right answer: the correct category was chosen, the refund amount matches, the agent did not call the delete tool. Fast, cheap and reliable.
LLM-as-judge
A second model grades outputs against a rubric, useful for qualities without an exact answer, such as helpfulness or tone. Calibrate it against human ratings before trusting it.
Human review
People grade a sample, especially for new agents and high-risk tasks. Human labels are also how you check the automated graders.
Evaluating across the AI stack
An agent can fail at several layers, and good evaluation tells you which:
- Retrieval and data: did the agent get the right context, or was the record missing or stale?
- Reasoning: did the model draw the right conclusion from correct data?
- Tool layer: did the tool call succeed, and was the tool the right one?
- Workflow: did approvals and handoffs happen where they should?
- Application: did the user get what they needed?
Monitoring agents in production
Offline tests catch regressions; production monitoring catches the cases you did not think of. Log every run with inputs, tool calls, outputs and outcomes. Track the rate of human corrections, escalations and failed tool calls, and alert when they rise. Sample and review runs every week.
Human-in-the-loop steps double as evaluation: every time a reviewer edits or rejects what the agent proposed, you have a labeled example of where it went wrong.
Where Jet Admin fits
Jet Admin builds AI agents and workflows on data you already have, from 200+ integrations, with approval steps where people review agent actions. Those reviews and the audit logs of agent actions, on the Business plan and above, give you a running record of what the agent did and where people corrected it, which is the raw material for evaluation.
Frequently asked questions
What is AI agent evaluation?
Measuring whether an AI agent completes tasks correctly, uses tools properly and stays within safety rules, before and after release.
How is evaluating an agent different from evaluating a model?
Model evaluation scores single responses. Agent evaluation scores a sequence of decisions and actions, including tool calls and their effects.
How many test cases do I need?
Start with 50 to 100 real cases covering common and edge situations, and grow the set with every production failure.
Can I trust LLM-as-judge?
For subjective qualities, once it is calibrated against human ratings. For anything with a right answer, use code checks instead.
Know what your agents are doing
Start with Jet Admin for free and build agents with review steps and logs on your own data.