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This paper introduces an agentic tool-making pipeline that enhances the efficiency of production LLM agents by compiling repeated procedural steps into validated, versioned tools, thereby eliminating the need for redundant code generation at inference time. By deploying this approach in a Fulfillment Center alarm-triage system, the authors achieved a 42% reduction in p50 latency and a 53% decrease in end-to-end error rates across 1,500 historical alarms. The use of structured verdicts from tools not only simplifies the architecture but also improves auditability and addresses specification gaps and data drift.
Self-evolving LLM agents can slash latency by up to 62% while significantly boosting reliability in industrial applications.
Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before deployment. The tool-maker grounds synthesis in the live environment as it collects execution traces, observes backend schemas and values, generates candidate tools, and repairs them against labeled cases. At runtime, the production agent calls these tools directly and falls back to code generation only when needed. We deploy the approach in a Fulfillment Center alarm-triage system, where an agent diagnoses alarms against a 44-node SOP over heterogeneous metric backends. In production, tool calls reduce p50 latency by 42%. On 1,500 historical alarms, they reduce end-to-end error rate by up to 53% by suppressing run-to-run variance in repeated steps. Because tools return compact structured verdicts, they also enable a simpler direct-call architecture, reducing p50 latency by a further 62% in a controlled ablation. Versioned tools also improve auditability and expose specification gaps and upstream data drift. Our results show that self-evolving agents can make industrial LLM systems faster, more reliable, and easier to operate.
