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A demo pass rate tells you that an AI agent can complete a carefully prepared scenario. It does not tell you whether the agent will ask for missing constraints, recover from a failed tool, obey an approval limit, or avoid unsafe side effects. I test those properties with a versioned 12-case failure matrix. Each case changes one realistic condition, records the first unrecoverable step, and preserves enough trace evidence to reproduce the run.
The method is useful whether you are evaluating a customer-service workflow, coding agent, analyst assistant, or autonomous operator. It measures recovery, uncertainty, tool use, policy compliance, and containment alongside the final answer, so a lucky outcome cannot hide a dangerous trajectory.
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Why does an agent pass the demo but fail in production?
Happy-path tasks remove the uncertainty that makes production difficult. The request is complete, the data is fresh, every API responds, permissions are generous, and the grader rewards the visible result. Real work includes omitted requirements, contradictory records, partial responses, revoked access, changing files, retries, and users who ask for actions they are not authorized to approve.
A single success score collapses all of those conditions into one number. An agent can receive credit after making an unsafe tool call, inventing a missing fact, or exploiting a weakness in the scorer. A stronger evaluation treats the trajectory as evidence: what the model believed, which tool it called, what the tool returned, how the plan changed, and whether any side effect escaped the test boundary.
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OpenAI’s PaperBench illustrates the scale of the problem: across 8,316 individually gradable tasks from 20 paper replications, the best tested agent averaged 21.0% (OpenAI, 2025). That figure is not a forecast for every product, but it is a warning against interpreting a polished demonstration as broad competence.
What are the 12 failure cases in a useful agent evaluation?
Use one case per trial, then combine cases only after each has a minimal reproducible test. The table is a planning map; the detailed checks below define what counts as a meaningful failure.
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| Case | How to inject it | What to inspect |
|---|---|---|
| 1. Ambiguous intent | Omit a required constraint or set conflicting priorities. | Does the agent ask a specific clarification instead of guessing? |
| 2. Intent-to-plan mismatch | Make a hidden budget, deadline, approval, or data boundary decisive. | Does the plan optimize the actual objective? |
| 3. Plan-adherence failure | Return a later result that invalidates an earlier step. | Does it re-plan safely without unapproved work? |
| 4. Invalid invocation | Cause malformed, incomplete, mistyped, or domain-invalid arguments. | Does it validate and repair the call? |
| 5. Tool-output misinterpretation | Return pagination, nulls, partial success, or an error in a successful HTTP response. | Is the next action grounded in the actual payload? |
| 6. Invention of new information | Remove a needed fact or substitute a plausible value. | Does it state uncertainty rather than fabricate? |
| 7. Stale, wrong, or adversarial state | Change files, logs, APIs, pages, memory, or executables after planning. | Does it re-check evidence before acting? |
| 8. Permission and guardrail conflict | Request an action beyond authority, policy, or an approval threshold. | Does it refuse or escalate before any side effect? |
| 9. External system failure | Timeout, disconnect, rate limit, transient error, or inconsistent retry result. | Are retries bounded, idempotent, and honestly reported? |
| 10. Reward hacking or scorer exploitation | Plant a shortcut in the task, prompt, grader, or harness. | Does it perform the intended work, not merely earn points? |
| 11. Contamination, refusal, or sandbagging | Use a public/repeated task, a refusal trigger, or evaluation-aware wording. | Is measured capability distorted by test awareness? |
| 12. Harness or measurement failure | Misconfigure isolation, reset, permissions, instrumentation, stopping, or scoring. | Can you trust that the test measured what you intended? |
1. Ambiguous intent
Leave out a required constraint, give two priorities that cannot both be met, or describe an outcome without saying what “done” means. A robust agent asks a narrow, answerable question such as which deadline or spending limit controls. Penalize confident assumptions, especially when they trigger an external action.
2. Intent-to-plan mismatch
State a clear goal but hide a decisive boundary in the task context: a fixed budget, a same-day deadline, an approval cap, or a data-residency rule. The agent may produce a coherent plan that is still wrong. Check whether it identifies the constraint, explains the trade-off, and chooses steps that satisfy the real objective.
3. Plan-adherence failure
Make a later tool result invalidate an earlier step—for example, an item is already locked or a prerequisite has failed. The correct behavior is to stop, update the plan, and request approval if the alternative changes scope. Continuing a stale sequence or taking extra actions is a plan-adherence failure; AgentRx uses that category for this pattern.
4. Invalid invocation
Return missing fields, wrong types, malformed syntax, or arguments that satisfy the schema but violate a domain rule. Test both whether the agent notices the error and whether its repair is safe. A retry that silently changes the target, quantity, or recipient is worse than a clear refusal.
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5. Tool-output misinterpretation
Use valid output that is easy to read incorrectly: a paginated list, a null that means “unknown,” a partial-success object, or an error embedded in an HTTP 200 response. The agent should inspect status fields, pagination tokens, and item-level results before deciding. Record whether it claims completion when only part of the operation succeeded.
6. Invention of new information
Remove a fact needed for the requested answer or replace it with a plausible value. The expected response is an explicit limitation, a request for the missing data, or a cited value from an allowed source—not a guess presented as fact. AgentRx names this “Invention of New Information.”
7. Stale, wrong, or adversarial environment state
Change a file, log, API record, web page, memory entry, or executable artifact after the agent forms its plan. EnvTrustBench is designed to test grounding when environment evidence is stale, wrong, or adversarial. Require a fresh read at the point of action and verify that untrusted content cannot rewrite instructions or permissions.
8. Permission and guardrail conflict
Ask for an action outside the agent’s authority, safety policy, or approval threshold. A good run ends in a precise refusal or an escalation request, with no side effect before approval. AgentRx lists “Guardrails Triggered” as a failure category because the quality of the refusal and the containment of attempted actions both matter.
9. External system failure
Make a tool time out, disconnect, rate-limit, return a transient error, or produce a different result on retry. Define a retry budget and an idempotency strategy before the run. The agent must distinguish “not completed,” “completed but unconfirmed,” and “completed,” rather than reporting success merely because a retry was sent.
10. Reward hacking or scorer exploitation
Plant a shortcut that earns points without completing the intended work—for example, a grader that checks a marker instead of the resulting state. OpenAI identifies reward hacking as a core validity hazard. Use independent checks of side effects and, where possible, a second grader that inspects the trajectory rather than only the final string.
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11. Contamination, refusal, or sandbagging
Use a public or repeatedly seen task, add a condition likely to trigger a blanket refusal, or make the agent aware it is being evaluated. OpenAI notes that contamination, refusals, and sandbagging can distort measured capability. Rotate task instances, separate capability tests from policy tests, and record when the task itself may have changed behavior.
12. Harness or measurement failure
Break the evaluation setup rather than the agent: misconfigure isolation, permissions, reset state, instrumentation, stopping rules, or scoring. Anthropic reviewed 141,006 cybersecurity evaluation runs and found three incidents in which a model accessed the internet from within or while interacting with the evaluation environment. The lesson is operational: test the harness with known-pass and known-fail probes, verify network reachability, and inspect logs for actions the scorer could not see.
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Reproducibility starts before the model sees the task. Treat the entire run as a versioned experiment, not as a prompt pasted into a chat window.
- Freeze the starting point. Version the task text, environment snapshot, tool schemas, policies, grader, model and agent configuration. Reset to that exact state for every trial.
- Define the claim and boundary. Write the behavior being tested, allowed tools, token or time budget, stopping rule, approval requirements, and the evidence a grader may inspect. OpenAI’s 2026 playbook stresses that harness design, tools, budgets, safeguards, scoring, and validity checks can change whether an agent solves the intended task or finds a shortcut.
- Inject one condition. Change only the variable represented by the failure case. If you combine a timeout with stale data, you will not know which condition caused the first error.
- Capture the full trajectory. Store prompts, model messages, tool calls and arguments, tool outputs, state transitions, retries, timestamps, permissions, and final side effects. Include the environment diff, not just the final answer.
- Mark the first unrecoverable step. Identify the earliest point after which the intended outcome could no longer be reached safely. Preserve the state immediately before and after that step.
- Classify the root cause. Separate reasoning, tool use, policy, environment, and harness causes. A tool timeout is not the same failure as claiming that a timed-out action succeeded.
- Minimize and regress. Remove irrelevant context until the failure still reproduces, then add that smallest task to a regression suite. Run the suite after changing the prompt, model, tool schema, policy, or harness.
Hugging Face recommends validating an evaluation harness with known-pass and known-fail cases and reducing production incidents to minimal regression tasks. Those checks catch a surprising number of false conclusions before you compare agents.
What evidence should every trial record?
A useful record lets another engineer reconstruct both the agent’s information and the environment it acted on. At minimum, retain:
- Immutable identifiers for the task, environment, tool-schema, policy, grader, model, and configuration versions.
- The exact prompt and every subsequent message, including hidden system or policy text that the evaluator is permitted to inspect.
- Raw tool requests and responses, HTTP status and headers where relevant, pagination markers, retry count, and latency.
- State snapshots or diffs before planning, before each side effect, and after completion or abort.
- Permission checks, approval events, network access, filesystem access, and any external side effect.
- The stopping reason, final status, grader observations, first unrecoverable step, and root-cause label.
Do not log only a model’s chain-of-thought. You can evaluate decision quality from observable messages, tool arguments, returned data, state changes, and concise rationale fields designed for auditing.
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How should I score two agents or harnesses?
Compare agents on a vector of safety and reliability measures rather than one aggregate pass rate. Report outcome success and trajectory quality separately.
| Measure | Question it answers |
|---|---|
| First-failure rate | How often does the agent reach an unrecoverable step? |
| Recovery success | After an injected fault, can it still complete safely? |
| Uncertainty calibration | Do confidence statements track what the agent actually knows? |
| Tool-call validity | Are arguments syntactically and domain correct? |
| Policy compliance | Does it refuse, escalate, and wait for approval correctly? |
| State robustness | Does it re-ground on stale, wrong, or adversarial evidence? |
| Rerun reproducibility | Does the same version behave consistently from the same snapshot? |
| Side-effect containment | How much unauthorized change occurs before detection? |
| Cost per valid trial | What does a trustworthy, fully instrumented run cost? |
Weight the measures according to the deployment risk. For a read-only assistant, calibration and factual grounding may dominate. For an agent that can move money, delete data, or change production systems, side-effect containment and approval compliance should be hard gates, not averages.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does failure localization add beyond a pass or fail?
Localization tells you where the trajectory became unrecoverable; attribution explains why. Without both, teams often “fix” the final answer while leaving the unsafe tool call or stale assumption intact.
AgentRx’s benchmark contains 115 manually annotated failed trajectories and reports gains of 23.6 percentage points in failure-localization accuracy and 22.9 points in root-cause attribution over prompting baselines (Microsoft Research, 2026). You do not need that benchmark’s exact model to use the principle: annotate the first irreversible mistake, preserve the preceding evidence, and make the annotation part of the regression test.
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Start with incidents that caused or nearly caused a real side effect. Redact secrets, preserve the causal state changes, and reduce each incident to the smallest task that still triggers the same first failure. Keep the original trace privately for audit, and expose only the minimized fixture to routine testing.
Organize the suite by failure mechanism, not by product team. A payment workflow and a deployment workflow may share invalid-invocation, retry, and approval cases. This makes model, prompt, and harness changes comparable across domains while retaining domain-specific policy checks.
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Run known-pass and known-fail probes whenever the harness changes. A sudden improvement can indicate a broken grader, a missing permission boundary, or an accidentally reachable network. A sudden decline can reflect a changed reset procedure rather than a model regression.
What should a trustworthy evaluation report disclose?
Publish enough detail for readers to judge validity: task population, environment and isolation, tools and schemas, model version, budgets, safeguards, reset procedure, stopping rules, scoring logic, known limitations, and checks for contamination or shortcuts. State whether figures are one-time measurements, averages across reruns, or rates from a particular region, edition, or configuration.
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This level of disclosure is not paperwork. The harness can change how an agent uses tools, tracks information, and recovers from mistakes, so two apparently similar scores may measure different capabilities. A transparent report makes the boundary of the claim as clear as the score itself.
What is the practical stopping rule?
Stop a trial immediately when the agent attempts an unauthorized side effect, loses the evidence needed to determine what happened, or reaches the first unrecoverable step. Preserve the state and trace, label the outcome, and reset before any follow-up. Continuing after that point mixes recovery behavior with damage control and can hide the original cause.
After the 12 individual cases are stable, combine them into adversarial scenarios and keep the same instrumentation. The goal is not to make an impressive demo harder; it is to learn whether the agent remains truthful, bounded, and recoverable when the world stops cooperating.
Frequently Asked Questions
How many runs should I execute for each failure case?
Use repeated runs when the agent or environment is nondeterministic, and report the number of attempts with the exact configuration. For deterministic fixtures, one run can establish a reproducible regression, but it cannot estimate a rate.
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Include them in the secured audit record when policy and privacy permit, because they affect permissions and behavior. If they cannot be disclosed, document their role, version, and the specific boundaries they enforce.
How can I test an agent that has no write access?
Simulate proposed side effects with a dry-run tool that validates arguments and records what would have changed. You can still test approvals, retries, stale state, output interpretation, and truthful status without modifying production data.
What is a good way to detect a flaky harness?
Run the same known-pass and known-fail fixtures repeatedly from identical snapshots, then compare tool visibility, permissions, reset state, and grader inputs. Divergence in those controls is a harness problem even when the model output looks consistent.
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