arXiv:2606.05806v1 Announce Type: new Abstract: Existing benchmarks evaluate Tool-Integrated Reasoning (TIR) in LLMs on idealized ''happy paths'', largely overlooking real-world tool failures. We introduce ToolMaze, a benchmark for dynamic path discovery and error recovery in TIR agents. To separate systematic replanning from blind trial-and-error, ToolMaze adopts a two-dimensional design: DAG-based topological complexity and a $2 \times 2$ taxonomy of tool perturbations (explicit/implicit, transient/permanent). Evaluations show that perturbations degrade performance across nearly all models,
The proliferation of LLM agents in real-world applications highlights the urgent need to address their resilience to tool failures, moving beyond idealized benchmarks.
Sophisticated readers should care because reliable autonomous AI agents are critical for unlocking productivity gains; their robustness in handling real-world imperfections determines their operational ceiling and adoption rate.
The focus of AI agent development shifts towards robust anomaly recovery and dynamic replanning, moving beyond 'happy path' testing to more complex, failure-aware environments.
- · AI agent developers
- · Enterprises deploying AI agents
- · AI safety researchers
- · Developers relying solely on idealized benchmarks
- · Early adopters of unhardened AI agents
Benchmarks like ToolMaze will become standard for evaluating the real-world readiness of LLM agents.
This will accelerate the development of more robust and fault-tolerant AI agent architectures and tool ecosystems.
Increased reliability of AI agents could lead to broader integration across critical infrastructure and complex white-collar workflows, escalating discussions around accountability and ethical deployment.
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Read at arXiv cs.AI