How we built agentic retrieval ragie.ai

We spent a lot of time at ragie.ai getting agentic retrieval right. Like a lot of stuff these days, the demo-quality version came together quickly, but the real work was making it reliable across domains with messy input data, and getting it to refuse instead of hallucinate when the source data wasn't sufficient to answer a query.

Classic RAG (embed and fetch) breaks down on compositional or scoped questions. Our approach treats retrieval as reasoning with multiple subagents in a loop: Plan → Search → Answer → Evaluate → Cite. This agent loop decomposes queries, chooses search strategies dynamically, and inspects intermediate results before responding.

I wrote up some details on how we implemented this and why. Hopefully some useful bits in there for anyone working on agents. Happy to answer any questions!