RAG vs Copilot vs Agent

Q: What is the difference between RAG, Copilot, and AI Agent?

RAG augments LLM responses with retrieved knowledge (Q&A, search). Copilot assists humans with AI-powered suggestions that require approval. Agents autonomously plan and execute multi-step tasks using tools. Complexity and autonomy increase from RAG → Copilot → Agent.

Q: Which should you build first: RAG, Copilot, or Agent?

Start with RAG — it is the simplest, has the clearest ROI, and the lowest risk. Copilots build on RAG by adding UI integration and workflow assistance. Agents are the most complex and should be attempted only after mastering RAG and copilot patterns.

Q: Can you combine RAG, Copilot, and Agent architectures?

Yes, and most mature systems do. An agent might use RAG as a tool for knowledge retrieval, present copilot-style suggestions for user approval, and execute approved actions autonomously. They are complementary, not competing patterns.