RAG vs Agentic RAG: same goal. Different failure modes. 🔧🧠

Classic RAG is simple: retrieve context → generate an answer.

Classic RAG is simple: retrieve context → generate an answer. Agentic RAG adds a planner that can loop, call tools, use memory, and chain multiple retrieval steps before it answers.

The signal that this is becoming real infrastructure: OpenAI, Anthropic, and Block just moved key agent standards into the Linux Foundation’s new Agentic AI Foundation—including MCP (tool/data connectivity) and AGENTS .md (how coding agents should behave in a repo).

MCP now has an official spec that’s evolving as a protocol layer for “models ↔ tools ↔ data.”

But here’s the tradeoff: more autonomy = more blast radius. OWASP still ranks prompt injection as the #1 risk for LLM apps, and the UK’s NCSC is blunt that it may never be “fully solved.”

Practical rule:

Use plain RAG when you need fast, explainable, low-risk answers.
Use Agentic RAG when the job needs actions—and lock it down with least privilege, approvals for destructive steps, and full audit logs. 🛡️

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