Verifiable Tool Action in AI Agents

Develop verifiable pre-execution validation mechanisms for tool calls issued by LLM-based AI agents (including API invocations, code execution, database writes, and web actions) that guarantee correctness, policy compliance, and safety before any side effects occur.

Background

The survey emphasizes that modern AI agents operate through tool calls that can have real-world side effects (writes, deployments, payments), making traditional text-only safety insufficient. While current practice relies on schemas, allowlists, prompts, and post-hoc critics, these mechanisms do not provide principled guarantees that an action is safe and correct prior to execution.

Formalizing verifiable action as a first-class requirement reframes agent governance: tools must expose contracts (preconditions and postconditions), and the agent must prove that these conditions hold before performing side-effecting operations. Solving this problem would provide stronger assurance and auditability for high-impact actions in enterprise, web, coding, and embodied settings.