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ArticleAgents

Agent evaluation before deployment

Test trajectories, side effects and recovery—not just whether the final answer looks correct.

By Nivoio Editorial1 August 202612 min
AG
The signal
  1. 01Score the trajectory as well as the outcome.
  2. 02Build tests from a concrete failure taxonomy.
  3. 03Gate rollout using risk-weighted results and live traces.

An agent can reach the right result through an unsafe path, or fail gracefully in a way a final-answer score misses. Evaluation has to inspect the work, not only the ending.

Define the unit of success

Begin with a task contract: initial state, allowed resources, required artifact, forbidden actions and completion checks. Outcome accuracy is only one dimension. Add policy compliance, tool correctness, evidence quality, cost, latency and human intervention. A useful evaluator can distinguish a correct answer reached with fabricated evidence from one reached through valid work.

For multi-step jobs, score checkpoints. Did the agent gather the necessary context before acting? Did it verify the target? Did it notice contradictory evidence? Checkpoints reveal where a run went wrong and make failures reproducible. They also show whether a model improvement changes reasoning quality or merely the final wording.

Build a failure taxonomy

Collect failures under operational categories: wrong goal interpretation, missing context, invalid plan, tool misuse, unauthorized action, stale memory, ungrounded claim, loop, premature completion and failed recovery. Each category needs examples and a severity. Without a taxonomy, teams accumulate anecdotes and optimize whatever failed most recently.

Turn production incidents and human corrections into regression cases. Preserve the smallest state that reproduces the failure and remove sensitive data. A growing suite of realistic cases becomes more valuable than a static benchmark because it reflects your tools, policies and users.

Test the environment, not only the model

Inject failures deliberately: timeouts, partial tool responses, duplicate events, unavailable permissions, stale records and conflicting instructions. Verify that retries are safe and that the agent can pause without corrupting state. Test what happens when a human rejects an approval or edits the plan halfway through.

Run adversarial cases against tool boundaries. Ask the system to exceed a spending limit, send to an unapproved recipient or treat retrieved instructions as authority. The expected behavior should be enforced by code where possible. An evaluation that only observes model compliance is testing politeness, not control.

Use rollout as an evaluation stage

Offline tests cannot reproduce every distribution shift. Start with shadow runs or read-only modes, then limited cohorts and reversible actions. Define automatic rollback signals before release: error rate, policy violations, repeated tool calls, cost spikes or unexpected approval frequency. Review traces, not just dashboards.

Promote a version only when it improves the risk-weighted profile. A small gain in task completion may not justify more severe failures. The goal is not a perfect agent; it is a system whose remaining failures are visible, bounded and recoverable.

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