A multi-agent RAG architecture I designed and built end to end: instead of a single model answering blind, an orchestrator routes each question to specialized agents, each scoped to a retrieval domain, and composes their results into a grounded answer.
The goal throughout was reliability at scale — keeping responses grounded in retrieved evidence rather than model memory, and keeping the system responsive under bursty, concurrent load.
Hybrid retrieval
Retrieval combines dense semantic search (embeddings over a vector store) with lexical matching, so the system catches both conceptually-related context and exact terminology. Candidates are reranked before they ever reach a model, which keeps prompts tight and answers anchored to real sources.
Agent orchestration
An orchestrator decomposes a request, dispatches to the agents best suited to it, and reconciles their outputs. Each agent is given only the tools and retrieval scope it needs — which keeps reasoning focused, makes behavior easier to evaluate, and sharply reduces hallucination compared to a single monolithic prompt.
Event-driven delivery
The system runs on a serverless, event-driven pipeline: requests flow through a managed message queue into stateless functions, so work fans out and scales automatically without idle infrastructure. That makes the pipeline resilient to traffic spikes and cheap to keep running.
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