How do you benchmark an AI agent that takes actions?

Define cases with expected end states, run them against isolated, reproducible fixtures with injected faults, independently verify the resulting system state, and report pass rate, verification outcomes, policy behaviour, retries, latency and cost with the methodology.

What is fault injection in agent testing?

Deliberately causing realistic failures — dropped writes, wrong totals, lost responses, transient server errors — in a test environment to check that the agent and its infrastructure detect them rather than reporting success.

Can benchmark results prove one model is better than another?

Only for the specific cases, configuration and conditions tested, and only with disclosed methodology. Results should not be generalised into broad superiority claims.

How to evaluate AI agent reliability

Benchmark AI agents on real outcomes: reproducible suites, fault injection, verification status, policy compliance, retries, latency and methodology.

By the Voidex team · Updated 13 October 2026 · 7 min read

Evaluate outcomes, not transcripts

Many agent evaluations grade the final message. For agents that act, the meaningful question is: after the run, is the business system in the expected state, and did the controls behave correctly along the way?

Design cases with explicit expectations

Happy paths — expected succeeded and verified.
Silent failures — the system acknowledges but drops the write; expected failed.
Transient errors — a 500 before the write; expected a safe retry and success.
Lost responses — the write happens but the response is lost; expected no duplicate.
Invalid input — expected rejection before execution.
Policy gates — sensitive and high-impact actions; expected awaiting_approval.

Make results reproducible

Version the suite; changing cases creates a new version.
Run each benchmark against freshly provisioned, isolated fixture environments.
Record a configuration hash covering tool versions, policy version and cases.
Compare runs side by side and flag when configurations differ.

Metrics that matter

Cases meeting expectation (pass rate).
Verified outcomes vs. failed or inconclusive verification.
Policy denials and approvals triggered.
Retries and duplicate-effect incidents (should be zero).
Latency percentiles, including queue time.
Model-provider cost, reported separately.

Publish the methodology with the numbers. A pass rate without the cases, faults and configuration behind it cannot be compared or trusted.

How Voidex does it

Voidex Bench runs versioned suites (JSON format voidex.bench.v1) on freshly provisioned simulator tenants with fault injection, records per-case outcomes, retries, policy denials and latency, hashes the configuration and compares runs. The included sample suite covers happy paths, dropped writes, transient errors, lost responses, invalid input and approval gates. It measures Voidex’s execution and verification behaviour; comparing real model providers will need provider adapters, which are planned.

Try it in the sandbox Read the API docs

Frequently asked questions

How do you benchmark an AI agent that takes actions?: Define cases with expected end states, run them against isolated, reproducible fixtures with injected faults, independently verify the resulting system state, and report pass rate, verification outcomes, policy behaviour, retries, latency and cost with the methodology.
What is fault injection in agent testing?: Deliberately causing realistic failures — dropped writes, wrong totals, lost responses, transient server errors — in a test environment to check that the agent and its infrastructure detect them rather than reporting success.
Can benchmark results prove one model is better than another?: Only for the specific cases, configuration and conditions tested, and only with disclosed methodology. Results should not be generalised into broad superiority claims.