Multi-Agent RAG Deployment Guide: Build a Three-Agent System on AWS Bedrock AgentCore (2026)

Why Multi-Agent RAG Outperforms Single-Agent Retrieval in Enterprise Settings As of May 22, 2026, B2B leaders face a familiar challenge: single-agent RAG systems often return documents that are tangentially related but fail to cite the exact source passages decision-makers need. In legal, compliance, and other high-stakes domains, citation accuracy can be as low as 60% on complex queries. The root cause? A single agent must simultaneously decompose the query, retrieve relevant documents, rank them, and generate citations — a cognitive overload that leads to noise. Enter multi-agent RAG. By splitting these responsibilities across specialized agents, you can dramatically improve both relevance and precision. Our recent test on a 50K-document legal corpus (SEC filings and case law) demonstrated a 40% improvement in citation accuracy when using a three-agent pipeline compared to a single-age

nt baseline. This article provides a vendor-neutral, step-by-step guide to deploying such a system on AWS Bedrock AgentCore, with real cost-per-query data and best practices for scaling. Architecture Overview: The Three-Agent Pipeline on AWS Bedrock AgentCore The system consists of three cooperative agents, each designed for a specific task: Query Decomposer (Qwen 3.8 Max): Breaks multi-faceted user questions into simpler sub-queries. Qwen 3.8 Max (available on Hugging Face as ) excels at instruction following and structured output, making it ideal for planning retrieval steps. Document Retriever (Llama 4): Executes each sub-query against the vector index. Llama 4 (from Meta, available via GitHub: ) provides robust text generation and can output natural-language retrieval criteria when combined with a tool call. Reranker (Cross-Encoder): A lightweight cross-encoder model (e.g., ) scores

and reorders retrieved passages for final answer generation. This agent outputs only the highest-confidence citations. The agents communicate through AWS Bedrock AgentCore’s built-in agent orchestration, which handles context sharing and sequential execution. No custom middleware required. Step-by-Step Deployment: Configuring Agents in Bedrock AgentCore Follow these steps to set up the three-agent pipeline on AWS Bedrock AgentCore. The instructions assume you have an AWS account with access to Bedrock and the necessary IAM permissions. 1. Prepare Knowledge Base and Vector Index Upload your document corpus (e.g., 50K legal documents) to an Amazon S3 bucket. Use Amazon Bedrock Knowledge Base to create a vector index with a supported embedding model (e.g., Amazon Titan Text Embeddings v2). Note the Knowledge Base ID for later use. 2. Create the Query Decomposer Agent In the Bedrock console,

navigate to Agents and create a new agent. Name: Select foundation model: Qwen 3.8 Max (ensure it is available in your region). Define a single action group with a tool that takes a user query and returns an array of sub-queries as JSON. Set agent instructions: "You are a query decomposition specialist. Break the user's question into up to 5 sub-queries that are specific and searchable. Output as JSON." 3. Create the Document Retriever Agent Create a second agent named . Select foundation model: Llama 4 (specify or equivalent). Attach the Knowledge Base created earlier as a tool. Agent instructions: "You receive a sub-query and retrieve the top 10 relevant passages from the knowledge base. Return the passages with source IDs and relevance scores." 4. Create the Reranker Agent Create a third agent named . Select foundation model: BAAI/bge-reranker-v2-m3 (available via Bedrock as a provis

ioned model or through SageMaker). Define an action group that calls the cross-encoder model API to score and reorder passages. Agent instructions: "Score each passage for relevance and re-rank them. Output the top 3 passages with their original source citations." 5. Configure the Multi-Agent Collaboration In Bedrock AgentCore, enable multi-agent collaboration for your supervisor agent (or use the built-in orchestrator). Add the three agents as sub-agents with execution order: Query Decomposer → Document Retriever → Reranker. Define hand-off criteria: the supervisor passes the original query and then collects outputs sequentially. 6. Test and Iterate Use the Bedrock test window to run sample queries. Monitor logs for each agent’s outputs and adjust instructions as needed. For production, enable CloudWatch logging to track latency and error rates. Cost-Per-Query Benchmarks: How Does Multi

-Agent Compare to Single-Agent? We benchmarked both architectures on a 50K-document legal corpus using 100 representative queries (average input length 50 tokens, output length 200 tokens). The table below shows average costs per query (USD) as of May 2026, based on AWS Bedrock on-demand pricing and