Retrieval-Augmented Generation (RAG) gets a major upgrade when you add agentic workflows — modular, intelligent systems where LLMs collaborate like agents in a pipeline. Here’s a visual breakdown I created to showcase:

🔹 Agentic RAG System
A router LLM determines whether to retrieve from the knowledge base. If yes, relevant documents are selected, evaluated, and turned into an augmented prompt for a final LLM with citation. If no, the LLM answers directly.

🔹 4 Agentic Workflow Patterns

1. Sequential Workflow – LLMs perform tasks step-by-step (parser → rewriter → citation → responder).
2. Conditional Workflow – Logic-driven routing where agents and retrieval paths are dynamically selected.
3. Iterative Workflow – Writer + Evaluator loop through multiple iterations before producing a final response.
4. Parallel Workflow – Multiple agents tackle sub-tasks simultaneously, coordinated by an orchestrator.

Each pattern opens new doors for building scalable, intelligent AI systems—whether you’re working on multi-agent AI, complex task execution, or enterprise RAG platforms.

🌐 Created with love at rachellearns.com

Would love to hear—
👉 Which workflow pattern are you most excited about?
👉 How are you integrating agents into your AI systems?

#AI #RAG #AgenticAI #LLM #MultiAgentSystems #MachineLearning #RetrievalAugmentedGeneration #LangChain #MindsOfTomorrow #rachellearns