From the source
When developers work with AI coding agents, they inevitably need information that exists outside the codebase.
API documentation, Stack Overflow answers, GitHub issues, framework tutorials: the web remains the authoritative source for much of what developers need to know.
Understanding what AI agents search for reveals both the capabilities and limitations of current agentic systems.
We asked Droid to analyze its own search behavior across 780,000 tool calls, and it took it from there: writing the SQL queries, building a classification pipeline, generating embeddings, and building the visualization below.
The results reveal how agents think: they discover broadly with search, then retrieve specifically with fetch, mirroring how humans research unfamiliar topics.
Documentation and learning materials dominate, with three quarters of all queries directly related to software development.
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Each point is a query, positioned using embeddings that Droid generated and reduced to 3D.
Similar queries cluster together naturally.
Methodology The entire analysis pipeline was built by Droid within Factory sessions.
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