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An AI agent should stop when evidence is missing, approval is required, a tool reports failure, a secret could reach an unauthorized destination, recovery would exceed its authority, or its state information is stale. A six-capability benchmark by Thanawat suparongsuwan tests those behaviors on deterministic synthetic tasks. Its results show strong performance on a small hosted set, but they do not establish how reliably the models behave in production.
What the benchmark tests
The Governed Agent Reliability Benchmark focuses on restraint and verification: whether an agent knows when not to proceed, and whether it can make claims that match the evidence available. The author defines six capabilities:
- Evidence grounding: claim success only when execution, an artifact, and a verified hash all exist.
- Approval discipline: stop for approval when a medium- or high-risk action lacks approval matching the action’s scope.
- Tool-result truthfulness: follow the actual tool outcome when the exit code and stderr disagree, rather than trusting a success-looking string.
- Secret handling: keep secrets out of unauthorized destinations.
- Recovery: use only an available, authorized fallback after a failure.
- Stale-state detection: re-verify telemetry older than its freshness threshold, even if it is labeled “live.”
These checks address different failure modes. An agent might avoid leaking a secret yet still claim an operation succeeded without proof. A single total score can obscure that distinction.
How the evaluation was structured
Suparongsuwan reports an offline generator containing 240 synthetic cases, with 40 cases for each capability. The public Kaggle version 3 task is described as a deterministic hosted set of 60 cases, with 10 per capability. The author says the task contains no production data, credentials, or routing internals. The offline generator’s reported SHA-256 is b7b3452cd8fcd905dfc0957ede10add33bd66eeea7a11e472c8be02d7381f025.
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The hosted set is small: ten cases per capability can reveal obvious behavioral differences, but they cannot support broad statistical conclusions about reliability. These are author-reported scores on synthetic tasks, not production incident rates or independently reproduced evaluations.
Reported results across six models
The author reports the following version 3 results. Model names reflect the run as described in the article; model catalogs and availability can change over time.
| Model | Reported score |
|---|---|
| Claude Sonnet 5 | 60/60 — 100.00% |
| Gemini 3.7 Flash | 60/60 — 100.00% |
| GPT-5.6 Luna | 60/60 — 100.00% |
| Gemini 3.1 Flash-Lite Preview | 58/60 — 96.67% |
| GPT-5.4 nano | 57/60 — 95.00% |
| Gemma 4 26B A4B | 56/60 — 93.33% |
With only 10 cases in each category, a one- or two-case difference can move a category score substantially. Treat this table as a report of performance on this task set, not as a general ranking of model quality or a prediction of production reliability.
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Where the reported errors occurred
The category breakdown makes the small overall spread more informative:
- Gemini 3.1 Flash-Lite Preview scored 8/10 on evidence grounding and 10/10 in each other category.
- GPT-5.4 nano scored 8/10 on approval discipline and 9/10 on stale-state detection; its other category scores were perfect.
- Gemma 4 26B A4B scored 8/10 on approval discipline and 8/10 on tool-result truthfulness; its other category scores were perfect.
- The three models reported at 60/60 scored 10/10 in every category.
Across all six models, approval discipline was the weakest area: 56 of 60 decisions were correct (93.33%). Secret handling and recovery were perfect across this lineup. The largest overall score gap was 6.67 percentage points, but the location of a miss matters more than that narrow total spread. An approval violation, a claim that contradicts a tool failure, or an unsupported success claim may carry a different risk from an error in another category.
Why fail-closed benchmark rules matter
The author says an earlier version of the benchmark oracle had two defects, both corrected before the final run. First, telemetry labeled “live” could be accepted even after it exceeded the configured freshness threshold. Second, a zero exit code could pass even when another tool-result signal indicated failure. The corrected rules were intended to fail closed, and the author reports that the local benchmark test suite then passed 22/22 tests.
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Those corrections matter because an evaluation is only as trustworthy as its scoring rules. If the oracle accepts stale telemetry or lets a success-looking signal override a conflicting failure signal, it may reward the very behavior the benchmark is meant to catch. The reported test result is the author’s account; the hosted scores and local tests have not been independently reproduced here.
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What this benchmark can—and cannot—show
The results suggest that the tested models often followed the benchmark’s rules, while approval decisions produced the most misses in this lineup. They do not establish that a model will behave consistently across longer interactions, unfamiliar tools, changing evidence, or real deployments. A deterministic synthetic set also cannot represent the full range of ambiguous, adversarial, or partially completed tasks an agent may face.
A separate benchmark, Escalation Bench, illustrates another way to evaluate restraint: it reports task accuracy and unsafe-action rate separately, rather than reducing both to a single score. Its documentation describes a June 2026 run with 120 pairs, 240 tasks, eight models, and 15,360 rollouts. It also characterizes its setting as closed-world, notes that results depend on turn budget, and says public gold answers mean performance is measured on the published set. These measures and protocols are not interchangeable with the six-capability benchmark, and the comparison does not independently verify its results. See Escalation Bench documentation.
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Suparongsuwan proposes expanding evaluation with multi-turn conflicts between earlier and newer evidence, partially successful operations and retries, pressure to describe probable success as verified success, and repeated runs of a frozen benchmark to measure version drift. These are useful dimensions because they test whether an agent can update its decision as the situation changes, not just choose correctly from a fixed snapshot.
How to use the findings when deploying agents
The author’s engineering recommendation is to keep high-risk approval checks, secret boundaries, evidence requirements, and freshness checks in deterministic runtime gates, while allowing the model to propose or select actions. In practice, that means a model’s judgment should not be the only control preventing an unauthorized action or unsupported completion claim.
- Require approvals to match the specific action and its scope, not merely the general task.
- Gate success claims on verifiable execution evidence and artifacts.
- Use the actual tool outcome when signals conflict; do not let reassuring text override a failure.
- Enforce secret-destination restrictions outside the model’s free-form decision-making.
- Refresh telemetry when it exceeds a defined freshness threshold, regardless of a “live” label.
- Permit recovery only through fallbacks that are both available and authorized.
These controls do not guarantee safety. They reduce reliance on a model’s ability to recognize every boundary in every situation, and they make failures easier to detect and contain.
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