Design a Research or Multi-Agent System
Research systems browse and synthesize with citations. Start with one agent and tools. Fan out extra agents only for independent, parallel reads — then cap loops, isolate tools, and trace every handoff.
Explain like I'm new to AI
The multi-agent lesson warned: extra personalities are extra cost and extra bugs. This is when the job is research (many sources, a brief) and when a team actually pays.
A single agent with search, browse, and a note tool. Parallelize reads with extra tool calls, not extra personalities. Default in 2026 until eval says otherwise.
Job: a sourced brief on a question. SLO: citation coverage, time budget, $ cap, no silent tool storms.
Shape: one agent + search/browse/notes is the default (2026). Multi-agent when sub-questions don't share state and can run in parallel.
Mental model
A journalist with a notebook vs a newsroom: you don't hire five editors to fetch one weather page. You do send two reporters to two cities.
How it works
- Tools: search, fetch, maybe a code interpreter in a jail. Fetch is indirect injection — no send-mail on that kit.
- Output: claims with source urls/ids. Verify citations in code when you can.
- Memory: a research pad (notes tool) rather than stuffing every page in the window (compress/isolate).
- Multi-agent: orchestrator assigns disjoint queries; synthesizer only sees notes, not god refund tools.
- Eval: rubrics on groundedness + coverage; humans on a sample. Cost per brief is a first-class metric.
- Traces: parent run id, child spans per gatherer.
Real-world example
"Competitive memo." One agent, 12 fetches, 8 minutes, $0.40, citations. The "team of 6" version spent $9, disagreed, and cited a URL it never fetched. Eval chose the single agent.
Technical explanation
Fan-out helps read-heavy, decomposable work (Anthropic-style guidance you've seen in the agents category). Tight sequential coding/support work gets worse with more agents.
MCP: each gatherer gets a read-only server. Orchestrator holds the write-to-doc tool.
Don't confuse this with AI search (next): search returns a ranked list; research writes a document.
Common mistakes
A Slack channel of agents with every MCP server installed because "collaboration."
- No $ or step cap.
- Synthesizer that can also hit production APIs.
When to use it
- Open-ended briefs over many sources. Fan-out when eval shows wall-clock or coverage wins.
When NOT to use it
- Don't multi-agent a FAQ. Don't skip citations because the prose sounds like a report.
Alternatives
- RAG chatbot over a closed corpus. Human researcher + RAG. Plain search UI.
Quick quiz
Related concepts
- MCP + Agents, IDEs, and Products — MCP is the plug; agents, IDEs, and products are hosts that decide when to call — a product can also publish a server so other hosts reach it.
- Planning & Reflection — Planning breaks a goal into sub-tasks before acting, and reflection lets the agent critique and fix its own work — together they make agents far more reliable.
- Tool, Retrieval, and Agent Traces — Nested spans for retrieve, each model call, and each tool — a waterfall that shows which stage failed, without dumping secret payloads.
Last reviewed: 2026-09-04 · Written by ByHeart AI · Reviewed by ByHeart AI