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If AI’s rapid pace feels overwhelming, trust me-everyone feels it. New models, new papers, new frameworks… it’s impossible to keep up with everything. And the good news is-you don’t have to. What actually helps is a clear path, not more noise. So I organized a 10-level roadmap for learning AI Agents-built to take you from the basics to real production systems without burning out. *Tip: Spend about 2–3 weeks per level. Build small projects, test ideas, and let concepts sink in. Go slower if you need. Go faster if you can. And when something new launches? Treat it as Level 11 and keep moving. *Your AI Agents Learning Roadmap Level 1: Foundations of GenAI & Transformers How tokens, embeddings, attention, and inference actually work. Level 2: Prompting & Model Behaviors CoT, ReAct, ToT, context design, prompting patterns, and jailbreak resistance. Level 3: Retrieval-Augmented Generation (RAG) Chunking, vector stores, retrieval pipelines, and what makes RAG good—or terrible. Level 4: LLMOps & Tooling LangChain, LangGraph, Dust, CrewAI, synthetic data, tools, and function calling. Level 5: Agents & Agent Frameworks Planning, memory, agent loops, LangGraph agents, CrewAI agents, and evaluations. Level 6: Memory, State & Orchestration Symbolic vs vector memory, persistent state, compression, and long-term context. Level 7: Multi-Agent Systems Decentralized systems, collaboration patterns, message passing, and agent teams. Level 8: Evaluation & RL LLM-as-a-Judge, RLHF, reward models, and self-improving agent loops. Level 9: Protocols & Safety MCP, agent-to-agent protocols, alignment, guardrails, and traceable autonomy. Level 10: Building & Deploying FastAPI, Streamlit, QLoRA, GGUF, caching, and monitoring with LangSmith/Arize/TruLens. *Save this. Build after every level. If you want tools to explore along the way: Start with Hugging Face (to explore LLMs/SLMs), use Ollama to run SLMs locally (Phi-4, TinyLlama), or try Fireworks AI to run bigger LLMs via API (Qwen 3, Kimi K2, DeepSeek R1). Then dive into LangChain and LangGraph (they’ll teach you 80% of the ecosystem), and later check out agentic frameworks like CrewAI or AutoGen. *Pro tip: Start with cookbooks-they’ll get you building faster than any tutorial.