Research: Verifiable Execution in Tool-Use and Function-Calling Agents: Methods and Eva...

Research question: How can verifiable execution mechanisms be integrated into tool-use and function-calling AI agents to improve reliability and trustworthiness?

Background & Motivation: Recent advances in large language models have enabled AI agents to interact with external tools and APIs via function calling, supporting complex reasoning and task automation. However, ensuring that these agents execute functions correctly and safely remains a critical challenge, especially in high-stakes applications such as healthcare or finance.

Research Gap & Question: While several frameworks exist for tool-use and function-calling, few provide formal or practical mechanisms for verifiable execution—that is, cryptographic or algorithmic guarantees that an agent’s claimed actions correspond to actual, auditable outcomes. This project asks how such verifiable execution can be systematically integrated and evaluated within agent architectures.

Approach & Expected Contribution: The study will survey existing approaches to verifiable computation, then propose and prototype extensions to an open-source agent (e.g., OpenAI function calling or LangChain agents) to log, verify, and audit each function execution. Experimental evaluation will compare baseline and verifiable agents on reliability, overhead, and robustness using synthetic and real-world tool-call tasks. The expected contributions are a comparative analysis, prototype implementation, and guidelines for integrating verifiable execution in AI agents.

Why It Matters: Robust verifiable execution mechanisms are essential for deploying trustworthy AI agents in critical domains. This research will help bridge the gap between advanced AI agent capabilities and the formal guarantees required by industry and society.