An agent with grep does quite well at search. It’s shocking to search technologists. We want to cocoon the problem in technology. We care about algorithms, knowledge graphs, ranking: ever-and-ever smarter retrieval. It turns out, though, you can play a bit of a trick. If you convince everyone to optimize content for your search engine, you’ll have built the best search engine. There’s Sutton’s bitter lesson about unleashing raw compute on a problem. But there’s a different bitter lesson in...

Agentic search gets interesting when agents do not know how to find the right answer. Oh, the agent might think it knows. It might confidently BS us. But the agent’s poor domain intuition steers itself astray. Agents make false assumptions about what our users think is relevant. Our fashionista users think “red shoes” should return high-heels. When I worked at one company ABE wasn’t a president, it was an A/B testing tool. Agents need context to know these things - and context engineering...

At Haystack I spoke about autoresearch: Code generation to optimize search rankers. Can we use it to improve on BM25? This article represents my lab notes. My agent starts with a BM25 implementation, proposes changes, and accepts those that improve NDCG. We’ll zero-in on passage retrieval dataset MSMarco. I won’t claim I’ve found a “better BM25” but I’ve iterated towards a decent tuning regime. All while learning valuable lessons about how validation data can leak. Let’s walk through what...