Multi-Agent RAG Systems

Multi-Agent RAG systems coordinate multiple specialized agents--planners, retrievers, tool-executors, critics, and synthesizers--to solve complex tasks that exceed the capabilities of a single retrieval-generation loop. Each agent has a clear contract (inputs, tools, and outputs) and communicates via messages or a shared blackboard, enabling concurrent retrieval and iterative reasoning.

              ┌─────────┐
              │ Planner │
              └─────────┘
                   ↓
             ┌───────────┐
             │ Retriever │
             └───────────┘
                   ↓
             ┌───────────┐
             │ Tool Exec │
             └───────────┘
                   ↓
             ┌───────────┐
             │ Generator │
             └───────────┘
           Feedback / Critic
            ┌──────────────┐
            │ Critic Agent │
            └──────────────┘
                   ↓
       revise plan / re-retrieve
      ┌─────────────────────────┐
      │ Blackboard / Memory Bus │
      └─────────────────────────┘
     - Messages, citations, scores
    - Shared state for coordination
┌──────────────────────────────────────┐
│ Parallel Retrievers: text/code/graph │
└──────────────────────────────────────┘
   ┌───────────────────────────────┐
   │ Tool calls: SQL, APIs, search │
   └───────────────────────────────┘
                   ↓
         ┌───────────────────┐
         │ Final Synthesizer │
         └───────────────────┘
                   ↓
           Answer + citations

memory bus.

1. Roles and protocols. The planner decomposes the task into subgoals; retrievers

query specialized indices; tool agents execute structured actions (SQL, web, vector search); a generator drafts hypotheses; and a critic evaluates faithfulness, coverage, and uncertainty. The synthesizer merges evidence and emits a final, cited answer.

2. Concurrency and scheduling. Multi-agent systems benefit from parallel retrieval

across modalities and indices. Schedulers allocate budgets (latency, tokens) per agent and cancel stragglers. A blackboard or message bus (e.g., Redis streams) enables decoupled coordination and backpressure control.

3. Planning strategies. Graph-based planners (DAGs), chain-of-thought with tool-use,

or PDDL-style operators can drive agent plans. Closed-loop planning incorporates critic feedback and retrieval uncertainty to replan when evidence is insufficient.

4. Safety and guardrails. Separate a policy/guard agent to enforce prompts, redact PII,

and validate citations. Critics run entailment or retrieval-grounding checks before answers are released. High-risk tasks require human-in-the-loop approval.

5. Scaling. Horizontal scale emerges naturally via agent pools with autoscaling. Use

semantic caching for repeated sub-queries and memoize tool results. Track per-agent KPIs (success, latency, cost) for adaptive routing.

6. When to use. Multi-Agent RAG is suited for research synthesis, incident response,

compliance analysis, and complex workflows that require multi-hop reasoning, tool use, and parallel retrieval. The trade-off is increased complexity and orchestration overhead.