arXiv:2605.28532v1 Announce Type: new Abstract: Tool-using agents often incur substantial computational cost due to long reasoning chains and iterative tool usage. In practical scenarios, many tasks become infeasible under constrained tool environments, where the capabilities required for successful task completion are unavailable. Detecting infeasible tasks and stopping execution early can significantly reduce unnecessary execution cost. In this work, we propose FeasiGen, an automatic pipeline for constructing infeasible agent tasks by identifying the critical tools required for successful ta
The proliferation of more complex AI agents and tool environments necessitates better computational efficiency and task management, making early infeasibility detection critical.
This research addresses a core limitation in current AI agent development, reducing computational waste, improving reliability, and enabling more effective deployment in real-world scenarios.
AI agents can now more intelligently assess their capabilities and the feasibility of tasks, leading to more efficient execution and a reduced likelihood of costly failures in constrained environments.
- · AI Agent developers
- · Cloud providers (reduced compute waste)
- · Industries deploying AI agents
- · AI agents without feasibility awareness
- · Inefficient compute models
Tool-using agents will experience reduced computational costs and faster task completion by avoiding infeasible operations.
This efficiency gain will enable the deployment of AI agents in more complex and resource-constrained environments, expanding their practical applications.
Improved agent reliability and cost-effectiveness could accelerate the development and adoption of fully autonomous AI systems across various industries.
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Read at arXiv cs.AI