Agent-to-Rule Distillation

Problem

Keeping a solved-often case inside the agent means re-deriving a known answer forever — at LLM latency and price, with a nonzero chance of fumbling it, and behind reasoning an auditor cannot replay. Writing the rule upfront was impossible because nobody knew the case existed until the agent surfaced it. The knowledge the agent earned in production stays locked in trace logs instead of hardening the process, and the system's cost and risk profile never improves with experience.

Solution

Instrument the agent so every case leaves a comparable trace: problem class, resolution path, outcome. Mine the traces for clusters where the same class was resolved along the same path repeatedly — on the order of ten identical resolutions is a workable trigger. For each stable cluster, author the deterministic equivalent (a DMN decision table, a BPMN branch, or plain code), validate it by replaying the historical traces it was mined from, and cut over so the rule short-circuits the agent for matching inputs. Keep the agent as fallback for anything the rule's guard rejects, and link each promoted rule to its source traces so its provenance stays reviewable. Repeated over time this inverts the system's economics: reported production programmes attribute cost reductions up to seventy percent to exactly this transition from dynamic reasoning to rule logic, and the agent's capacity concentrates on genuinely novel cases.

When to use

• Production traces show the agent resolving the same problem class along the same path repeatedly.
• Cost, latency, or auditability pressure makes paying model price for known answers unacceptable.
• A deterministic home for promoted logic exists — a process engine, rule engine, or plain code path with a fallback route to the agent.