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Failure-Transparent Agents: Benchmarking Post-Failure Reporting in Tool-Using Language Models

Junru Zhu, Shiming Xie, Aime Lu Fan Chen, Xiaoqing Ding, Chunxin Tang, Ruoyu Qi, Yulang Fei

arXiv:2609.35732v1cs.AI

TL;DR

Tool-using agents can report success without evidence after a tool failure, a behavior that existing benchmarks often entangle with other agent capabilities. FTA fixes the failed evidence state and audits post-failure claims. Across six models, the evidence-contract policy was associated with lower unsupported-claim rates while useful-response rates remained high in this blocked-task benchmark.

  • Problem

    Existing benchmarks often entangle post-failure reporting with tool selection, recovery, and environment dynamics, leaving the fidelity of reports after known failures difficult to isolate.

    Source: §1 · p. 1

  • Method

    FTA fixes the failed-tool observation and evaluator-side evidence requirements before generation, then scores final claims and useful recovery against that evidence state.

    Source: §1 · p. 1, §2.1 · p. 2

  • Results

    False-success rates were 22.8% under baseline, 9.3% with transparency, and 0.8% with the evidence contract across six models and 3,600 responses.

    Source: §1 · p. 1

  • Takeaways & Limitations

    The tested evidence-contract policy is associated with lower unsupported-success rates while useful responses remain high within blocked-task scenarios.

    Source: §6 · p. 4

    FTA uses synthetic, English-only, predominantly one-step tasks without real-world trace validation or matched successful-tool controls.

    Source: §5 · p. 4

Abstract

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Tool-using agents can fail twice: a required tool can fail, and the agent can then report success without the evidence needed to justify it. Existing benchmarks often entangle this reporting failure with tool selection, recovery, and environment dynamics. We introduce Failure-Transparent Agents (FTA), a controlled benchmark that fixes the failed observation and required evidence state before generation, making post-failure claims directly auditable. FTA contains 100 tasks with deterministic failure traces spanning five failure families, a neutral control, and four user-pressure conditions, and evaluates unsupported claims alongside useful recovery. Across six models, three response policies, and 3,600 human-annotated responses, false-success rates are 22.8% under the baseline policy, 9.3% with a transparency instruction, and 0.8% with a structured evidence contract. Fabricated-detail rates decrease from 28.3% to 14.3% and 0.8%, while useful responses increase from 74.9% to 89.2% and 98.8%, respectively. The tested evidence-contract policy is associated with substantially lower post-failure reporting errors while useful-response rates remain high within this blocked-task benchmark.

1. INTRODUCTION

Tool-using agents can compound tool failures by claiming outcomes unsupported by observed evidence, while existing benchmarks often entangle post-failure reporting with other agent capabilities. FTA fixes the failed evidence state before generation and audits responses; across six models, the evidence contract had the lowest false-success rate while useful responses remained high.

1. INTRODUCTION

  • Existing benchmarks evaluate agent reliability alongside tool selection, interaction, recovery, or environment dynamics, motivating a narrower test of reporting after known failures.The question is whether models faithfully report what was and was not observed given the same failure and missing evidence.Source: §1 · p. 1
  • FTA fixes deterministic failed-tool observations and evaluator-side evidence requirements before generation, making final claims auditable independently of tool choice or retry strategy.It measures false success and fabricated detail while also recording recovery and usefulness so blanket refusal is not treated as reliability.Source: §1 · p. 1
  • The benchmark comprises 100 deterministic post-failure tasks and a 3,600-response, six-model evaluation of the tested structured reporting policy.The authors report that the policy is associated with lower false-success rates while useful-response rates remain high in blocked-task scenarios.Source: §1 · p. 1
  • False-success rates were 22.8% under baseline, 9.3% with transparency, and 0.8% with the evidence contract across six models and 3,600 human-annotated responses.Fabricated-detail rates decreased from 28.3% to 14.3% and 0.8%, while useful responses increased from 74.9% to 89.2% and 98.8%, respectively.Source: §1 · p. 1, Figure 1 · p. 2

2. FTA BENCHMARK

FTA fixes the post-failure evidence state and evaluates whether responses preserve the boundary between observed and unavailable evidence. Its synthetic tasks vary failure family and user pressure, and scoring covers unsupported claims, useful recovery, and annotation procedures.

2.1. Controlled Post-Failure Evaluation

  • FTA scores whether a response claim is warranted by the evidence exposed after a known failure, rather than measuring end-to-end agent competence.The model receives the user request and failed-tool trace; evidence requirements, recovery options, and useful partial help remain evaluator-side.Source: §2.1 · p. 2, §2 · p. 2

2.2. Task Construction

  • FTA contains 100 synthetic tasks across five failure families, balanced over a neutral control and four user-pressure conditions.Deterministic replay returns the same tool observation, while distinct scenario contents across conditions make condition comparisons descriptive rather than isolated causal effects.Source: §2.2 · p. 2

2.3. Diagnostic Metrics and Metadata-Blinded Human Annotation

  • Scoring distinguishes false success and fabricated detail from limitation disclosure, feasible recovery, useful response, and over-refusal.False success is an unsupported claim of success; fabricated detail is concrete content requiring evidence the model did not receive.Source: §2.3 · p. 3
  • All 3,600 responses are manually scored under a fixed rubric, but annotation reliability cannot be directly assessed because independent double-annotation and agreement are not reported.Annotators do not see model identity or policy metadata, although structured contract fields can make the condition inferable.Source: §2.3 · p. 3

3. EXPERIMENTAL PROTOCOL

The protocol compares three response policies on the same scenarios and failed observations, using two model cohorts and descriptive pooled analyses. The structured evidence contract specifies response fields, while the post-confirmatory extension and non-crossed prompt conditions limit inferential claims.

3.1. Response Policies

  • The evidence contract requires STATUS, EVIDENCE, LIMITATION, and NEXT ACTION fields, whereas the transparency instruction requests disclosure and an appropriate next step.The contract is a response policy applied to the same scenarios, not an evaluator or verification stage.Source: §3.1 · p. 3

3. EXPERIMENTAL PROTOCOL

  • All three response policies receive the same request and deterministic failed-tool observation, enabling paired comparisons conditional on a fixed evidence state.The policies are baseline, a transparency instruction, and a structured evidence contract.Source: §3.1 · p. 3, §3 · p. 3

3.2. Model Cohorts and Response Collection

  • An original three-model cohort and an independent post-confirmatory three-model extension contribute 1,800 responses each, totaling 3,600 across six models.The extension is analyzed separately and checks whether the qualitative policy ordering persists across additional models.Source: §3.2 · p. 3

3.3. Statistical Analysis

  • Six-model pooled rates and prompt-condition comparisons are descriptive, and the contract-versus-baseline false-success interval clusters resampling over 100 semantic task clusters.Prompt conditions do not cross identical task content, and the extension is post-confirmatory rather than an enlargement of the original analysis.Source: §3.3 · p. 3

4. RESULTS

Across the tested response policies, structured evidence reporting is associated with fewer unsupported post-failure claims and more useful responses. Errors also vary across prompt conditions, while the policy ordering persists in the independently collected extension.

4.1. Post-Failure Errors Vary Across Prompt Conditions

  • Forced-choice scenarios reach an 85.0% baseline false-success rate, while neutral, urgency, and expected-answer conditions are near zero.Because prompt conditions involve different scenario contents, these differences are diagnostic and do not isolate a causal effect of wording.Source: §4.1 · p. 3

4.2. Structured Evidence Reporting Is Associated with Lower Unsupported Claims

  • False success falls from 22.8% under baseline to 9.3% with transparency and 0.8% with the structured evidence contract.Fabricated detail decreases from 28.3% to 14.3% and 0.8%, respectively; the contract is bundled, so the experiment does not identify which component drives the difference.Source: §4.2 · pp. 3–4, Table 1 · p. 4

4.3. Lower Error Rates Preserve Useful Responses

  • Usefulness rises from 74.9% under baseline to 89.2% with transparency and 98.8% with the evidence contract, within this blocked-task benchmark.Without matched successful-tool controls, these results do not establish the absence of false blocking when tools succeed.Source: §4.3 · p. 4
  • The lower false-success rate associated with the evidence contract persists across additional tested models despite substantially different baseline rates.The independently collected extension is descriptive generalization evidence rather than an enlargement of the original cohort.Source: §4.3 · p. 4

5. DISCUSSION AND LIMITATIONS

FTA treats visible tool failure and unsupported reporting as distinct reliability concerns. Its controlled policy comparison shows a reporting gap, while bundled prompts and constrained tasks limit causal attribution and ecological coverage.

  • Visible tool failure does not ensure faithful reporting: unsupported success claims remain common under some prompt conditions.Source: §5 · p. 4
  • The evidence contract is associated with consistently lower false-success rates and high useful-response rates, but bundled changes prevent attributing the difference to one component.Source: §5 · p. 4
  • Fixing failure before generation improves auditability but excludes tool selection, retries, changing environments, and long-horizon planning, limiting ecological coverage.Source: §5 · p. 4
  • FTA uses synthetic, English-only, predominantly one-step tasks without real-world trace validation or coverage of partial success, contradictory evidence, multi-agent interaction, or extended trajectories.Source: §5 · p. 4

6. CONCLUSION

Tool failure and post-failure reporting are distinct reliability dimensions. FTA fixes failed execution and audits final claims against evidence; tested policies are associated with lower unsupported-success rates while useful responses remain high within blocked-task scenarios.

  • FTA fixes the failed execution state and audits final responses against an explicit evidence boundary.Source: §6 · p. 4
  • Across 3,600 human-annotated responses, tested transparency and evidence-contract policies are associated with lower unsupported-success rates while useful-response rates remain high within blocked-task scenarios.Source: §6 · p. 4
  • Agent evaluation should assess whether user-facing claims are warranted by evidence actually obtained, not task completion alone.Source: §6 · p. 4
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