arXiv:2606.07904v1 Announce Type: new Abstract: Tool-augmented large language model agents increasingly rely on external APIs, but standard tool schemas describe how to call a tool, not when the tool is causally appropriate or what task state it produces. Causal tool filtering addresses this gap by using lightweight contracts that specify each tool's preconditions, effects, risk level, and cost. However, manually writing and maintaining such contracts does not scale to large or changing tool ecosystems. We introduce Contract2Tool, a framework for inferring tool contracts from metadata, schemas
The rapid expansion of tool-augmented LLM agents highlights the growing need for more reliable and scalable methods to manage complex tool ecosystems. This research directly addresses a critical bottleneck in deploying robust AI agents.
This development moves towards more autonomous and reliable AI agents by automating knowledge acquisition for tool usage, which is essential for scaling sophisticated AI applications beyond human-curated interfaces. It enables AI systems to independently understand and utilize new tools.
The burden of manually engineering tool contracts for AI agents is reduced, allowing for quicker integration of new tools and more dynamic agent capabilities. It accelerates the development cycle for advanced AI agents.
- · AI Agent Developers
- · API providers
- · Enterprise Software
- · AI-driven Automation
- · Manual API Integration Services
- · Legacy Workflow Automation
AI agents become more capable and less prone to errors when using external tools.
The development and deployment of complex, multi-tool AI agents will significantly accelerate across various industries.
This could lead to a proliferation of highly autonomous AI systems managing critical operations, increasing both efficiency and potential systemic risks.
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.AI