Multi Agent RAG Platform Implementation on AWS

Service Overview

Traditional single‑LLM chatbots often hallucinate or time‑out when facing complex, multi‑step requests. Recent guides from AWS & LangChain highlight how chaining agents with RAG eliminates those gaps by letting one agent plan, others retrieve or call APIs, and a final agent synthesize an answer.

Our engagement delivers that architecture end‑to‑end:

• Bedrock Agent Orchestration – We stand up Amazon Bedrock Agents that invoke domain‑tuned Anthropic Claude or Llama 3 models plus tool‑use actions for vector search, SQL, or third‑party APIs 
• Knowledge‑Base & Vector Store – Configure Amazon Kendra or OpenSearch Vector with chunking, embeddings, and freshness policies—inspired by Logic20/20’s Marketplace RAG pattern
• Multi‑Agent Workflow Layer – Implement CrewAI‑style agent crews with ReAct reasoning so specialists (e.g., “Researcher”, “Critic”, “Formatter”) collaborate on each query
• Observability & Guardrails – Add Bedrock Guardrails plus CloudWatch dashboards to track vector hits, tool calls, latency, and toxicity filters
• Pilot Use‑Case Delivery – Typical PoC integrates two data domains (e.g., PDFs in S3 and Confluence pages) and exposes a chat UI or API, mirroring the quick‑win strategy used by Marketplace RAG chatbots

Most clients reach production in 4–6 weeks, similar to timelines discussed in Hugging Face’s multi‑agent cookbook.

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