The Reliability Gap in Tool-Augmented Agents

Modern LLM agents often struggle with tool selection and execution because they lack a formal understanding of the operational requirements for the tools they use. When an agent treats a tool as a black box, it frequently fails to satisfy necessary preconditions (e.g., required input formats, state dependencies) or misinterprets the side effects of an action. Contract2Tool addresses this by introducing a framework that learns and enforces formal 'contracts'—preconditions and effects—for each tool, ensuring that the agent only invokes functions when it is safe and logical to do so.

Learning and Enforcing Operational Contracts

The core of the Contract2Tool approach is the transition from implicit prompt-based tool descriptions to explicit, learned constraints. By analyzing historical execution data and tool documentation, the system derives a structured contract for each tool.

• Preconditions: These define the state or input requirements that must be met before a tool is triggered. By explicitly checking these, the agent avoids runtime errors and invalid API calls.
• Effects: These define the expected state changes following a successful execution. This allows the agent to verify whether an action achieved the desired outcome, enabling better planning and error recovery.

By integrating these contracts into the agent's reasoning loop, the system significantly reduces 'hallucinated' tool usage and improves the robustness of multi-step task completion. This shift moves agent architecture toward a more deterministic, software-engineering-focused paradigm where tool interaction is treated as a contract-based interface rather than a probabilistic guess.