A single agent searching a single knowledge base is the clean textbook case. Real organizations are messier: engineering docs, sales content, HR policies, legal contracts — distinct domains, each with its own knowledge base, its own vocabulary, and often its own access rules. Trying to serve all of them with one agent over one giant blended index degrades quality for everyone. Multi-agent RAG is the pattern for this reality: specialized agents, each expert in a domain, coordinated by an orchestrator that routes each query to the right one. Here's how to structure it.
Why not just one big index?
The tempting simplification is to dump everything into one knowledge base and let one agent search it all. It underperforms for concrete reasons:
- Retrieval quality suffers from blending. A query about "onboarding" might mean engineering onboarding or HR onboarding. In a blended index, retrieval returns a muddle of both, and the agent can't cleanly disambiguate. Domain-scoped retrieval is sharper.
- Different domains need different handling. Legal retrieval needs exact-term precision and completeness; a casual product FAQ doesn't. A specialized agent can be tuned — its prompts, its retrieval settings, its guardrails — for its domain. A generalist can't be optimal for all of them.
- Access control is per-domain. HR and legal content have different permission rules than public product docs. Keeping domains separate makes it far easier to enforce that a query only reaches knowledge the user is entitled to.
So multi-agent RAG isn't complexity for its own sake — it's a response to the genuine heterogeneity of organizational knowledge.
The orchestration patterns
1. Router (the workhorse). An orchestrator classifies the incoming query and routes it to the single most relevant specialized agent, which retrieves from its domain knowledge base and answers. This is the simplest and most common pattern, and it handles the majority of cases: most queries belong to one domain, and routing them there gives sharp, domain-tuned retrieval.
- The key challenge: accurate routing. Misroute a query and the right knowledge base never gets searched. Routing can be done by an LLM classifier, by embeddings (which domain is the query nearest?), or a hybrid. Handle the "belongs to none / belongs to several" cases explicitly.
2. Orchestrator-with-aggregation. Some queries genuinely span domains: "What's our policy on X and how is it implemented?" might need both the HR agent and the engineering agent. Here the orchestrator dispatches to multiple specialized agents, collects their results, and synthesizes a combined answer.
- The key challenge: aggregation. Merging results from multiple domains coherently — resolving overlaps, attributing sources correctly, producing one answer rather than stapled-together fragments — is harder than routing to one.
3. Hierarchical / agent-as-tool. Specialized agents can themselves be exposed as tools to a higher-level agent, which reasons about which to call, in what order, and how to combine them — closer to a general agentic workflow. Most powerful, most complex, and the easiest to over-build. Reserve it for genuinely multi-step cross-domain reasoning.
Where MCP fits naturally
Multi-agent RAG maps cleanly onto MCP. Each domain knowledge base can be exposed as an MCP server with a search tool; the orchestrating agent treats each as a callable tool and decides which to invoke. This is a clean architecture because:
- Each knowledge base is an independent, reusable server — the same engineering-docs MCP server can serve the orchestrator and be connected directly to a developer's IDE.
- Adding a new domain means adding a new MCP server, not rewiring the orchestrator's internals.
- Access control lives at each server's authorization layer, per domain.
With Kognita, each knowledge base already exposes an MCP server, so "one specialized agent per knowledge base, orchestrated by a router" is a natural shape rather than something you assemble from scratch — the servers exist; you build the routing on top.
Keeping it from spiraling
Multi-agent systems are where complexity and latency explode if you're undisciplined:
- Start with routing, not full orchestration. A router to specialized agents handles most real needs. Add multi-agent aggregation only for the cross-domain queries that genuinely require it, and hierarchical orchestration only if you truly have multi-step cross-domain reasoning.
- Bound the fan-out. Dispatching to many agents per query multiplies latency and cost. Route to the few that matter, not all of them by default.
- Preserve provenance across agents. When results come from multiple domains, each claim still needs to trace to its source knowledge base. Aggregation must not launder away citations.
- Make routing observable. When an answer is wrong, you need to see which agent handled it and whether the query was routed correctly — a misroute and a bad retrieval are different bugs with different fixes.
The takeaway
Multi-agent RAG exists because organizational knowledge is not one homogeneous pile — it's distinct domains with different vocabularies, quality needs, and access rules. The winning structure is usually simpler than it sounds: specialized agents, one per knowledge domain (each cleanly an MCP server), fronted by a router that sends each query to the right one, with multi-agent aggregation reserved for the queries that truly span domains. Resist the pull toward elaborate hierarchical orchestration until you've confirmed you need it. Get routing right over well-scoped, per-domain knowledge bases, and you deliver sharper answers than one generalist agent over one blended index ever could — while keeping access control and provenance intact across the whole system.