Skip to content
← Back to explorer

Beyond Function Calling: Benchmarking Tool-Using Agents under Tool-Environment Unreliability

Yang Tian, Zhengpeng Shi, Bo Zhao · Jun 24, 2026 · Citations: 0

How to use this page

Moderate trust

Use this for comparison and orientation, not as your only source.

Best use

Background context only

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Moderate

Derived from extracted protocol signals and abstract evidence.

Abstract

Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments. Although recent tool-use benchmarks increasingly cover complex task settings, they still largely assume clean, stable, and trustworthy tool environments, leaving tool-environment unreliability insufficiently examined. We introduce ToolBench-X, a benchmark for evaluating agents under recoverable reliability hazards. ToolBench-X contains executable multi-step tasks across diverse domains and sequential, parallel, and mixed workflows, each paired with deterministic tools and a canonical final answer for automatic evaluation. Starting from clean tool environments, ToolBench-X injects five structured hazard types: Specification Drift, Invocation Error, Execution Failure, Output Drift, and Cross-source Conflict. Crucially, each injected instance remains solvable through at least one valid recovery path, such as retrying, fallback, verification, or cross-checking. Experiments reveal a substantial reliability gap: agents that perform well with reliable tools often fail under recoverable hazards. Further analysis shows that failures are driven less by tool-use volume or inference budget than by limited hazard diagnosis and ineffective recovery. Targeted recovery hints recover many failed tasks, while test-time scaling yields more limited gains. These results suggest that tool-use evaluation should move beyond function-call accuracy toward task completion under unreliable tool environments. The code and data is available at https://github.com/Foreverskyou/ToolBench-X.

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A benchmark-and-metrics comparison anchor.

Main weakness

No major weakness surfaced.

Trust level

Moderate

Usefulness score

25/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 55%

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments."

Evaluation Modes

strong

Automatic Metrics

Includes extracted eval setup.

"Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments."

Benchmarks / Datasets

strong

ToolBench

Useful for quick benchmark comparison.

"We introduce ToolBench-X, a benchmark for evaluating agents under recoverable reliability hazards."

Reported Metrics

strong

Accuracy

Useful for evaluation criteria comparison.

"These results suggest that tool-use evaluation should move beyond function-call accuracy toward task completion under unreliable tool environments."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Not reported
  • Expertise required: Medicine, Coding

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: Tool Use, Long Horizon
  • Quality controls: Not reported
  • Evidence quality: Moderate
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

ToolBench

Reported Metrics

accuracy

Research Brief

Metadata summary

Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments.
  • Although recent tool-use benchmarks increasingly cover complex task settings, they still largely assume clean, stable, and trustworthy tool environments, leaving tool-environment unreliability insufficiently examined.
  • We introduce ToolBench-X, a benchmark for evaluating agents under recoverable reliability hazards.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics, Simulation environment) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Research Summary

Contribution Summary

  • Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments.
  • We introduce ToolBench-X, a benchmark for evaluating agents under recoverable reliability hazards.
  • These results suggest that tool-use evaluation should move beyond function-call accuracy toward task completion under unreliable tool environments.

Why It Matters For Eval

  • We introduce ToolBench-X, a benchmark for evaluating agents under recoverable reliability hazards.
  • These results suggest that tool-use evaluation should move beyond function-call accuracy toward task completion under unreliable tool environments.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Pass: Benchmark or dataset anchors are present

    Detected: ToolBench

  • Pass: Metric reporting is present

    Detected: accuracy

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

Get Started

The #1 talent network for AI training.

Self-Service
Post a job, get a curated shortlist
Manage your team directly on-platform
Managed Service
Most popular
Dedicated program lead for your project
We source, vet, and onboard your team
Freelance AI Trainer?
Join the #1 platform for finding AI training and data labeling work.