Solver-Aided Verification of Policy Compliance in Tool-Augmented LLM Agents
This addresses the lack of reliable policy compliance in sensitive applications like customer service and business process automation, representing an incremental improvement over existing steering methods.
The paper tackles the problem of ensuring tool-augmented LLM agents comply with domain-specific operational policies by introducing an SMT solver-aided framework that translates policies into formal constraints and blocks violating tool calls, reducing policy violations while maintaining task accuracy as evaluated on the TauBench benchmark.
Tool-augmented Large Language Models (TaLLMs) extend LLMs with the ability to invoke external tools, enabling them to interact with real-world environments. However, a major limitation in deploying TaLLMs in sensitive applications such as customer service and business process automation is a lack of reliable compliance with domain-specific operational policies regarding tool-use and agent behavior. Current approaches merely steer LLMs to adhere to policies by including policy descriptions in the LLM context, but these provide no guarantees that policy violations will be prevented. In this paper, we introduce an SMT solver-aided framework to enforce tool-use policy compliance in TaLLM agents. Specifically, we use an LLM-assisted, human-guided approach to translate natural-language-specified tool-use policies into formal logic (SMT-LIB-2.0) constraints over agent-observable state and tool arguments. At runtime, planned tool calls are intercepted and checked against the constraints using the Z3 solver as a pre-condition to the tool call. Tool invocations that violate the policy are blocked. We evaluated on the TauBench benchmark and demonstrate that solver-aided policy checking reduces policy violations while maintaining overall task accuracy. These results suggest that integrating formal reasoning into TaLLM execution can improve tool-call policy compliance and overall reliability.