Multi-Agent RAG: How It Works and When You Need It

Single-agent RAG vs multi-agent RAG

Basic RAG is a fixed pipeline: embed the question, fetch the closest chunks, pass them to a model, return the answer. Nothing in it makes decisions. Single-agent RAG puts one model in a loop with tools. The agent decides whether to search, which tool to call, whether the results are good enough and whether to search again. Our guide to the RAG agent covers that pattern in detail.

Multi-agent RAG takes the same loop and splits it across several agents. Each has its own instructions, its own tools and, most importantly, its own context window. They talk through messages: the planner sends a task, the specialist returns findings. The split is the whole point and the whole cost. An agent with a narrow job and a clean context tends to do that job better. But nothing one agent learned is known to the others unless someone writes it into a message.

The call counts in the table are rough estimates for typical designs, not benchmarks. Yours depend on how many specialists run per question and how often the checker sends work back.

The usual roles in a multi-agent RAG architecture

Frameworks name these roles differently, but most multi-agent RAG systems come down to four.

Router or planner

A router makes one decision: which specialist should handle this question. A planner does more. It breaks a question such as "Why did enterprise churn rise last quarter and what did those customers complain about?" into sub-questions, assigns each to a specialist and decides when enough has been gathered. Routing is cheap and often needs only a small model or even a classifier. Planning needs your strongest model, because every later step inherits its mistakes.

Retrieval specialists, one per source

Each specialist owns one source and knows how to query it well. A document specialist rewrites the question for hybrid search and applies metadata filters. A data specialist knows the warehouse schema and writes SQL. A ticket specialist knows which fields in the helpdesk matter. This is where the split pays off: the prompt that makes a model good at SQL over your schema is long and specific, and you do not want it sitting in the context of an agent that is searching PDFs. Permission checks belong here too, enforced in the tool code for the current user, never left to the prompt.

Synthesizer

The synthesizer writes the answer from what the specialists return. Its two jobs are to resolve conflicts (the policy document says 30 days, the CRM note says 45) and to attach each claim to a source. It should receive the original passages or rows with their ids, not only the specialists' summaries of them.

Checker

The checker reads the draft answer next to the evidence and looks for claims without support, missing parts of the question and citations that point to the wrong passage. If it finds a gap, it sends the work back to the planner with a description of what is missing. It works best when it has not seen the reasoning that produced the draft, so it cannot be talked into the same mistake. Cap the number of retries, usually at one or two, or a hard question becomes an expensive loop.

Not every box has to be an agent. A router can be a classifier, a checker can be a single model call with a rubric. Anthropic's guide to building effective agents draws the same line: workflows run through predefined code paths, agents direct their own process. Many "multi-agent" designs turn out to be mostly workflow with one or two real agents inside. That is a good thing, since fixed steps are cheaper and easier to test. For the layers underneath (ingestion, chunking, indexes, reranking), see our RAG architecture guide.

What multi-agent adds: cost, latency and new failure modes

Cost

Every agent is at least one model call, and each call carries its own instructions, tool descriptions and retrieved text. In its engineering write-up on its multi-agent research system, Anthropic reported that agents typically use about 4 times more tokens than chat interactions, and multi-agent systems about 15 times more. The same write-up reports the upside: on Anthropic's internal research evaluation, a system with a Claude Opus 4 lead agent and Claude Sonnet 4 subagents outperformed a single Claude Opus 4 agent by 90.2%. Both numbers describe open-ended research, which is the friendliest case for multiple agents. On narrow questions over one corpus you pay much of the cost and see little of the gain.

Three habits keep the bill in proportion: use small models for routing and for specialists with simple jobs, let the planner skip agents a question does not need, and cache prompts that repeat.

Latency

Parallel specialists help: three searches at once take as long as the slowest one. But the planner, the synthesizer and the checker run one after another, and each adds a full model call. As an illustration, if one call takes 3 seconds, a plan, a parallel retrieval round, a synthesis and a check take about 12 seconds, and one retry pushes that past 20. That is fine for a research report and too slow for a support chat. Streaming the final answer improves how the wait feels, but the user still sees nothing until the plan and retrieval rounds finish, unless you show progress steps.

Failure modes you did not have before

• Wrong routing. The question goes to the document specialist when the answer is in the database. The specialist returns something plausible, and nobody downstream knows a better source existed.
• Lossy handoffs. A specialist summarizes ten passages into three sentences and drops "for contracts signed before 2024". The synthesizer states the rule without the condition.
• Vague delegation. The planner sends "look into churn" with no time range or segment. Two specialists do overlapping work and neither answers the actual question.
• Compounding errors. A small early mistake becomes the premise for every later step. Anthropic's write-up puts it plainly: agents are stateful and errors compound.
• Checker failure in both directions. A lenient checker approves everything and only adds cost. A strict one rejects correct answers and triggers retries that double the bill.
• Injection that travels. Instructions hidden in a retrieved document can now influence an agent whose output other agents trust. Treat every specialist's output as untrusted text, and keep permissions and actions in code.

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A decision rule: when one agent with several tools is enough

Anthropic's advice for agent builders is to find the simplest solution possible and add complexity only when needed. For RAG, the simplest agentic design is one agent with one tool per source: search_docs, query_orders, get_tickets. It handles more than most teams expect. Our rule: stay on one agent until you can name a failure in your traces that a separate role would fix.

The conditions below are the ones that usually justify the move. Tick those that are true for your system today.

Two cases point the other way. If the sub-tasks depend on each other, so that each step needs everything the previous one found, splitting them into agents mostly creates handoff loss. Anthropic notes that domains where all agents must share the same context are not a good fit for multi-agent systems today. And if you need answers in two or three seconds, a planner and a checker will not fit in the budget.

There is also a middle path that covers many real products: keep one agent, put a cheap router in front of it to select the tool set, and add one grounding check behind it. You get most of the routing and checking benefit for two extra calls. If you do go further, our comparison of the best RAG agent frameworks covers the orchestration options.

How to evaluate and debug a multi-agent RAG system

A multi-agent system rarely takes the same path twice, so you cannot test it by comparing steps to a script. You test outcomes, and you record everything so that a bad outcome can be traced to the agent that caused it.

1. Build the single-agent baseline firstRun it on the same questions. It is the number every extra agent has to beat, and often it shows that the real problem is retrieval.
2. Start with a small evaluation setAnthropic describes starting with about 20 queries that represent real usage. Early changes have large effects, so a small set is enough to see them. Grow it with every failure you find in production.
3. Score end to endFor each answer: is it correct, is every claim supported by a cited passage, does it cover the whole question. A model-based judge with a written rubric scales this. Check a sample of its scores by hand before you trust it.
4. Score each agent separatelyRouting accuracy against labeled questions. Retrieval recall and precision per specialist. For the checker: how many planted unsupported claims it catches, and how many correct answers it rejects.
5. Trace every run under one idRecord each agent's input, tool calls, output, token count and duration. Without this you see only a wrong answer and six prompts that might be responsible.
6. Track cost and latency with qualityReport tokens and seconds per question, including the 95th percentile. A change that lifts accuracy by two points and doubles the cost is a product decision, not a clear win.
7. Remove agents to prove themTurn off the checker or one specialist and rerun the set. If the score does not move, the agent is cost without benefit.

When an answer is wrong, read the trace backwards. Was the claim supported by the evidence the synthesizer received? If not, the synthesizer invented it and the checker missed it. If the evidence was wrong or incomplete, did the specialist retrieve badly, or was it sent to the wrong source or given a vague task? Fixes usually land in one of three places: the planner's delegation instructions, the tool descriptions and the retrieval layer itself. The metrics and tooling are covered in our RAG evaluation guide.

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Where Gilzor fits

The decision in this article, one agent or several, is the kind we would rather settle with measurements than opinions. We build custom RAG systems, add RAG to existing products, and consult on and evaluate RAG systems, including the question of whether a multi-agent design earns its cost over a single-agent baseline. Our AI work includes a payroll platform that extracts data from PDFs and cut salary calculation errors by 17%, and QLode, an AI fashion commerce product with image and text search. We work from Poland with partial overlap with the US working day, and a 2-week fixed-price Discovery Sprint is a practical way to scope the sources, the evaluation set and the simplest architecture that passes it.