ACORN: Compliant and Efficient LLM Agents via Contract-Based Symbolic Control

Large language model (LLM) agents augmented by tools are increasingly deployed to execute standard operating procedures (SOPs), acting on external systems through tool calls. However, prompt-based guidance leaves compliance to the model, while blocking guardrails may lead to repeatedly rejected actions without advancing the task. In addition, when the SOP rules are sufficient to already determine the next call, querying the model only adds error and cost. We present ACORN (Agentic COntract-based Runtime coNtrol), an agent framework that leverages contract-based symbolic control to address these challenges. The required behaviors are formalized as assume-guarantee (A/G) contracts in linear temporal logic on finite traces (LTLf) over the agent's tool-call trace, i.e., the sequence of tool calls and returns. The contracts are compiled to deterministic finite automata (DFAs). Given the fixed tool set of an agent session, the framework uses the DFAs before each tool call to select the subset of tools that the contracts currently permit and exposes it to the model. When the subset leaves only one possible call, the call is executed without consulting the model. Any tool call that would violate a contract or leave some contract impossible to satisfy is blocked. Each selected subset of tools is cached and reused in later sessions, whenever they reach the same DFA states and recorded values. On ten SOP-Bench domains, ACORN commits no procedural violation, improves macro task success by 23.1 percentage points, and reduces estimated cost by 62%. On two τ²-bench domains, it improves pass^4 by 15.0 points on average.