From Trajectories to SOPs: Reusable Procedures for Reliable Agent Execution

Large language model (LLM)-based multi-agent systems often rely on repeated online reasoning, while the procedural knowledge in their execution trajectories remains tied to individual tasks. We propose an SOP-centric framework that abstracts shared execution logic from teacher-generated trajectories into a codebook of reusable standard operating procedures (SOPs). Each SOP specifies procedural steps, input-output contracts, and verification rules, enabling procedures to be retrieved, composed, and verified across tasks. A large-model Overseer performs SOP-level planning and dispatches compact latent codes grounded in this codebook, while lightweight Executors follow the composed procedures with runtime verification, local repair, and selective escalation. Across AgentSkillOS, SkillsBench, and SWE-bench Verified, our framework improves the macro-average score by 33.1% over the strongest baseline. With a 9B executor, it improves performance by 79.4% over Vanilla while reducing allocated GPU-seconds per task by 18.6%. Compared with natural-language plans, latent SOP dispatch reduces serialized communication payload by a factor of $63.88$ without sacrificing task quality. Ablation and perturbation studies support the importance of SOP abstraction, contract verification, and runtime recovery for reliable execution. These results highlight reusable procedures as an effective interface between large-model planning and lightweight execution.