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Measure an AI agent’s reliability by running it repeatedly on representative tasks, verifying the actual outcomes, and testing how it handles changed wording, realistic tool failures, and adversarial inputs. Report success alongside consistency, safety, cost, latency, and the exact test setup: a persuasive transcript or one benchmark score cannot show whether an agent will work dependably in deployment.
Define what “reliable” means for the task
Start with the decision the evaluation must inform: for example, whether an agent can book appointments, resolve support requests, or complete a coding task with a specified level of oversight. State which users and situations the test is meant to represent, what counts as success, and which risks matter. A score is meaningful only in relation to that claim.
NIST AI 800-2, an initial public draft dated January 2026, frames benchmark evaluation around defined objectives and whether the benchmark fits them. Automated benchmarks do not answer every assurance question; red teaming, field testing, and post-deployment monitoring may also be needed.
Verify outcomes, not just transcripts
For a task with a verifiable end state, inspect the system where the outcome should have occurred. A booking task, for instance, should be checked against the booking record—not graded as successful because the agent said it had booked the appointment. Inspect the agent’s tool calls and parameters as well, so a correct-looking result does not hide an unsafe or unauthorized action.
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For work without a simple pass/fail state, define a rubric before testing. Break quality into explicit criteria, such as completeness, factual accuracy, adherence to constraints, and suitability for the intended user. Choose a grader that fits the claim:
- Code-based checks: fast, objective, and reproducible when the required end state can be expressed precisely. They can be brittle if the task has valid alternatives the checks do not recognize.
- Model-based grading: useful for nuanced answers, but potentially nondeterministic. Compare its judgments with expert human reviews and calibrate it before relying on its scores.
- Human review: can apply expert judgment to ambiguous or high-stakes outputs, but takes time and is costly. Use consistent criteria and review procedures.
Measure repeatability and robustness
Run each task multiple times under controlled conditions and report the number of successful runs out of the total. Include the distribution by task, not only a pooled pass rate: a high overall score can conceal one important task that fails consistently. Report uncertainty where appropriate and distinguish a single successful attempt from performance that recurs.
Then vary the request without changing its intended meaning. Rephrase it, alter representative context, and test equivalent inputs. If success collapses when wording changes slightly, the agent is not robust to ordinary variation. ReliabilityBench proposes pass-k analysis for repeated executions; treat that analysis and its experimental findings as specific to the paper’s setup, not as a universal estimate.
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Clean runs do not reveal how an agent behaves when its tools or environment misbehave. Inject realistic faults—such as timeouts, rate limits, partial responses, or schema changes—and record whether the task completes, whether recovery is safe, how many retries and extra turns occur, and the resulting latency and cost. OpenAI notes that harness decisions, including retries and state preservation, affect observed performance.
Evaluate security separately from ordinary task success. Test prompt-injection or hijacking attempts and other threats relevant to the agent’s tools and permissions. Record both whether an attack succeeds and what consequence follows; an aggregate attack-success rate can hide meaningful differences between scenarios. NIST CAISI also cautions that attacks should adapt to the system being tested.
Study results illustrate why these conditions matter, but they are not general reliability rates. NIST CAISI reported that, in its tested AgentDojo Workspace red-team setup, its strongest new system-tailored attack raised attack success from 11% for the strongest baseline attack to 81%. ReliabilityBench’s preprint reports success falling from 96.9% at ε=0 to 88.1% at ε=0.2 in its own experiments, and identifies rate limiting as its most damaging fault in ablations. Those findings apply to the tested tasks, systems, and conditions; they should not be projected onto other agents.
Compare efficiency as well as success
Track operational cost and latency beside task outcomes. For conversational agents, useful measurements can include turns, tool calls, tokens, retries, and elapsed time. When repeated attempts allow a success rate to be estimated, expected cost per successful solve is more informative than cost at a fixed token budget alone.
Keep the setup equivalent when comparing models, frameworks, or harness configurations. Match task set, environment state, tools, permissions, budgets, scoring rules, repetitions, and review. Disclose those choices so readers can tell whether the comparison tests the agent or a difference in the surrounding setup.
| Dimension | What to measure | What it reveals |
|---|---|---|
| Verified outcome | Runs that reach the expected external state; correctness of tool calls and parameters | Whether the task actually completed as intended |
| Consistency | Passes across repeated runs, including task-level results and variation | Whether success is repeatable or sporadic |
| Robustness | Success under equivalent wording and representative context changes | Sensitivity to ordinary input variation |
| Fault tolerance | Completion, safe recovery, retries, extra turns, latency, and cost under injected faults | How the agent behaves when tools or services fail |
| Safety and security | Attack success and severity by scenario | Whether adversarial inputs cause harmful or unauthorized outcomes |
| Efficiency | Cost per successful solve, latency, turns, tool calls, and retries | The operating burden of achieving correct results |
| Evidence quality | Grader validity, human calibration, representativeness, contamination checks, and reproducibility | Whether the score supports the claim being made |
Check that the evaluation itself is trustworthy
A test can produce a precise score while measuring the wrong thing. Review transcripts and task artifacts for reward hacking or grader gaming, check for task or answer contamination, and identify broken tasks, ambiguous prompts, or unreliable tools. Report refusals when they affect the result, and consider whether the agent recognizes evaluation contexts or sandbags.
NIST CAISI defines evaluation cheating as exploiting a gap between a task’s intended measurement and its implementation in a way that subverts validity; examples include accessing solution information or exploiting a scoring loophole. OpenAI’s 2026 account offers a concrete illustration: human review of GPT-5.4 evaluation attempts reduced an initial roughly 13-hour time-horizon estimate to about 6 hours after reward-hacked successes were excluded. That is an example of review changing a reported estimate, not a general reliability statistic.
Use separate suites for improvement and regression
Capability evaluations can probe difficult tasks to identify where an agent needs improvement. Regression suites serve a different purpose: they check whether tasks that previously worked still pass, and can be run continuously to catch changes over time. Keep the two purposes distinct so a challenging capability score is not mistaken for a stability check—or vice versa.
For deployment decisions that extend beyond the tested benchmark, complement automated results with methods suited to the real environment, such as red teaming, field testing, or ongoing monitoring. NIST AI 800-2 is an initial public draft rather than a final standard; IEEE P3777 is an active standards project, and NIST’s evaluation-probes work is ongoing. ReliabilityBench is a preprint whose displayed date information is inconsistent with its arXiv identifier, so its results are best identified by the paper title and identifier rather than assigned an unverified publication year.
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