Agentic RAG in 2026: Building Self-Correcting Retrieval Pipelines with MCP and Knowledge Graphs

Sun, 03 May 2026 00:00:00 +0000

The RAG Problem Nobody Talks About
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By mid-2026, nearly every serious AI application uses some form of Retrieval-Augmented Generation. But if you’ve deployed RAG in production, you know the dirty secret: most RAG pipelines fail silently. They retrieve documents that look relevant but don’t actually answer the question. They hallucinate citations. They return stale data from indices that haven’t been updated.

The 2024-era “embed → retrieve → stuff into prompt” pipeline is dead. What’s replaced it is Agentic RAG — a paradigm where the retrieval process itself is driven by an AI agent that reasons about what information it needs, evaluates the quality of what it finds, and iterates until it has a trustworthy answer.

Knowledge Graphs on XiDao Tech Blog

Agentic RAG in 2026: Building Self-Correcting Retrieval Pipelines with MCP and Knowledge Graphs

The RAG Problem Nobody Talks About #

The RAG Problem Nobody Talks About
#