Beyond Simple RAG: Designing Self-Correcting Multi-Agent Workflows

Simple RAG works well for direct questions. But once a task requires comparing figures, connecting information across documents, or resolving conflicting sources, top-k retrieval starts to show its limits.

1. Where Vector Search Falls Short #

Embeddings measure semantic similarity, not factual correctness. A document can sound relevant while still being the wrong evidence for a specific question.

2. The Multi-Agent Pattern #

Separate the workflow into distinct responsibilities:

1 Retrieval Agent: Gathers evidence from vector databases, SQL tables, or live APIs.

2 Evaluation Agent: Scores evidence relevance and filters out low-confidence data before synthesis.

3 Generation Agent: Produces answers strictly from evidence that passes verification.

3. Verify Before You Generate #

Separating evaluation from generation gives the system room to reject weak context, retrieve more evidence, and reduce unsupported answers.

The goal of multi-agent architecture isn't complexity for its own sake. It is structure: a reasoning process that can be tested, observed, and improved.