There's a comfortable assumption behind a lot of "agent + knowledge base" work: garbage in, garbage out. Feed an agent a messy, stale, duplicate-ridden wiki and it'll confidently tell users the wrong thing. So we invest in dedup, freshness, clean ingestion — to stop the agent from hallucinating.
I built a small testbed to measure that assumption, and it's wrong. Or rather: it's wrong about how bad ingest hurts, and the real answer is more interesting — and harder to catch.
The setup
I built a tiny agent that navigates a wiki the way a person does: it has a
wiki_search tool and a wiki_read tool, it decides what to look up, reads a page, and answers. No vector database, no RAG injection — the agent navigates. (This is increasingly how capable models prefer to work: they know what they need better than a query-time embedding match.)
