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The distinction you’ve drawn (the decision is made by the deterministic engine; the LLM merely explains it) matches something I’ve seen in another field. I was testing the agent’s memory: the schema of a vehicle changes mid-run, and the agent must make a call appropriate to the new schema at the end. Results: - A 20-line ‘the latest definition wins’ rule: 40/40 - Providing the entire history to the model: 38/40 - Retrieval-based memory: Mem0 (open-source) 3/15, TF-IDF + recency 1/40 When presented with the correct information, the model used it without issue. What it couldn’t reliably do was decide which information was still valid. I’m curious about versioning on your end. When a rule changes, what happens to decisions made under the old rule? If someone asks six months later, “Why was this application rejected?”, does the explanation refer to the rule version at the time of the decision, or to the current one? Does the RAG side know that a policy passage has become invalid, or could it retrieve the old document to explain a new decision?