a multi-agent system for search
an upgrade from static retrieval 🔎
standard retrieval-augmented generation (rag) changed the ai game by giving llms an “open-book exam” for every query, but traditional rag relies on static retrieval.
before searching, documents are chopped into isolated text fragments. this introduces a context gap, which is a major flaw. if a user asks a complex, open-ended question, a static search engine just blindly grabs fragments based on keyword similarity. it can’t connect dots from different pages, follow emergent leads, or realize that a critical piece of information is missing the surrounding context.
static retrieval treats research like peering into a keyhole.
to fix this, anthropic introduced a multi-agent research system. instead of a single, static search, this approach treats research like a collaborative human team using an orchestrator-worker pattern.
technically, this works with an iterative loop across three core layers:
the orchestrator (lead agent)
when a complex query arrives, a dominant llm analyzes it, maps out a strategic research plan, and breaks the problem into distinct subtasks.parallel subagents (workers): the lead agent spawns specialized subagents in parallel, each with its own context window, custom tools, and specific research mandate (ex. one hunts web sources and another analyzes internal files).
information compression: simultaneously, these specific subagents act as intelligent filters. they autonomously execute multiple tool calls, follow new leads based on their findings, and compress their raw data into high-value insights.
finally, they feed these condensed pieces back to the lead agent, which synthesizes the fragmented findings into one deeply contextualized answer. by replacing static databases with a multi-agent network, ai search upgrades from ctrl+f to iterative investigation.
today’s drops 🔎
check out this deeptech buildathon hosted with soma capital on june 5th
design internship @ the city of new york
off-season applications have opened with founders, inc.