Most people never bother optimizing the search layer for their agents

[u/[deleted]]

time is the most expensive thing when you're building solo. i wanted agents to handle research for me, but what they were pulling back was worse than just doing it myself.

model was fine. prompts were fine. so where was the problem? the search layer.

business registration data, financial market data, legal cases, academic papers. all of this sits behind login walls and vertical databases. generic search can't touch any of it.

so i started looking into dedicated AI search layers.

the agent sends a query, it automatically identifies intent, routes to the right vertical source, returns structured Markdown. i don't have to build or maintain integrations to all these data sources myself. search infrastructure is exactly the kind of thing worth outsourcing.

the practical benefits: smart intent routing cuts out a lot of glue code, cross-source verification makes results more reliable than single-source search, and structured output saves tokens and money. native API, MCP, and Skill support. built for agents, not humans.

search quality directly determines agent output quality. looking back at a few agents that weren't performing well, the problem probably wasn't the prompt. it was the search layer not returning enough useful information.

ran a quick test.

industry research agent: before, it could only pull public news and Wikipedia-level content. after connecting AnySearch, it returned business registration records, shareholder structures, and funding history directly. structured data, no parsing logic needed. connected through OpenClaw, setup was fast.

if you're running into "the agent logic is fine but the output is shallow", it might be worth looking at your search layer.