Beyond Function Calling: Benchmarking Tool-Using Agents under Tool-Environment Unreliability
arXiv:2606.25819v1 Announce Type: new 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 work
The rapid advancement and deployment of large language models are pushing the boundaries of AI agent capabilities, necessitating robust evaluation under realistic conditions.
This benchmark addresses a critical vulnerability in autonomous agents by simulating real-world tool unreliability, which is essential for safe and effective deployment across various industries.
The focus shifts from merely successful tool interaction to resilient, recoverable interaction, directly influencing future AI agent design and deployment strategies.
- · AI agent developers
- · Cloud providers
- · Software testing industry
- · Industries deploying AI agents
- · Companies with fragile AI deployments
- · Developers ignoring real-world unreliability
- · Benchmarking tools lacking realism
AI agents become more robust and reliable in complex, real-world environments.
This reliability accelerates the adoption and expansion of AI agents into critical infrastructure and sensitive workflows.
The increased autonomy and reliability of AI agents could significantly reshape labor markets and industry structures.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL