How do you teach an agent your company's knowledge without fine-tuning?

I'm building a multi-agent ops system for a logistics company (real one, \~100 vehicles). It runs on a local model (Qwen 2.5 14B on a Mac mini) and is grounded in our production database. The agents are good at live facts: where a parcel is, what a merchant owes. They query the DB and never make numbers up. But they have no idea how the company actually *works*: our procedures, which merchant needs a phone call instead of a message, our payment rules. None of that is a row in a table. It lives in people's heads. So the question I keep turning over: how do you give an agent your company's institutional knowledge? Three roads: 1. **Fine-tune.** Off the table for me. No hardware, no clean dataset, and a fine-tuned model can't tell you where it learned a fact, which breaks my one hard rule: never say something you can't trace to a source. 2. **Connect to the DB directly.** Great for live data. Does nothing for rules and procedures, and raw DB access is risky. 3. **A retrieval layer the agent looks things up in.** Knowledge lives in an ordinary DB, the agent searches it like it searches for a parcel. Change a rule = edit one row. Every fact keeps its source. Swap the model anytime. I went with 3, with 2 alongside it for live data. The DB holds today's numbers, the knowledge layer holds how the company works. The part I think is the actual idea: when an agent hits something it doesn't know, instead of guessing (never) or logging a dead end, it opens a question to the right staff member (routed by role, never sees their contact details), captures the answer as a draft, a human approves it, and it becomes permanent knowledge. Next time anyone hits that gap, the answer is there. It interviews the company while it runs. One thing I almost got wrong: I first wanted staff answers added automatically. Caught it, one careless reply becomes a confident wrong answer the agent trusts forever. So everything waits for a human approval. What I'd genuinely like to be argued with on: * For a small setup, is retrieval really the strongest of the three, or am I rationalizing what I can afford? * The hard failure mode: detecting "I don't know" when the model answers *confidently* but is missing context. Empty retrieval is easy. Confident-but-wrong is not. Anyone solved this without it crying wolf? * Approving every new fact by hand is safe but could become the bottleneck that kills the loop. Where would you let low-risk knowledge auto-approve? Anyone built a knowledge layer for a real agent system in production? Curious where yours broke.