Roadmap To Learn Agentic AI

What Is Agentic AI?

Agentic AI = LLMs (like GPT-4/5) + Autonomy + Tools + Memory + Planning.

In simple terms, it’s AI that can:

• • -Think and reason (using LLMs)
• • -Plan actions toward a goal
• • -Use tools (APIs, databases, browsers, etc.)
• • -Remember past interactions
• • -Collaborate with other agents
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Examples:

• ChatGPT with tools & memory

• AutoGPT, BabyAGI

• Devin (AI software engineer)

• AI customer support or trading agents

  
  

  
  

Agentic AI Learning Roadmap (2025)

Phase 1: Foundations (1 month)

Learn Python for AI:

• Data structures, functions, OOP

• Async programming (asyncio)

• APIs (requests, aiohttp)

• Environment setup (virtualenv, Poetry)

  
  

  Core Libraries:

• langchain

• openai / anthropic

• transformers (Hugging Face)

• pydantic, fastapi

  


Learn Prompt Engineering:

• Basic prompting techniques

• Chain-of-thought reasoning

• Few-shot, zero-shot prompting

• Function calling and structured outputs

Goal: Build simple LLM apps — e.g., “Document summarizer,” “Q&A bot.”

Phase 2: Large Language Model (LLM) Fundamentals (1–1.5 months)

Understand LLM Architecture:

• Transformer architecture

• Attention mechanism

• Tokenization & embeddings

• Fine-tuning vs. prompt-tuning

  
  

Frameworks to Learn:

• Hugging Face Transformers

• OpenAI API (GPT models)

• Ollama / LM Studio for local models

• LangChain / LlamaIndex for orchestration

  
  

Practice:

• Build simple chatbots using OpenAI API

• Use embeddings for semantic search

• Use LangChain for RAG (Retrieval-Augmented Generation)

  
  

Goal: Understand how LLMs “think” and how to control their reasoning.

Phase 3: Tool Use and Reasoning (1 month)

Learn Agent Frameworks:

• LangChain Agents

• CrewAI

• LlamaIndex Agents

• AutoGen (Microsoft)

• OpenDevin (for autonomous coding)

  
  

Key Concepts:

• Tool definition (tools in LangChain)

• Multi-step reasoning

• Planning and reflection

• Memory (short-term, long-term)

• Agent workflows

Practice Ideas:

• Web-searching agent (using SerpAPI)

• File-reader agent

• Email summarizer agent

• Multi-agent chat simulation

  
  

Goal: Build an AI agent that can use tools like Google, Notion, or Python code to complete tasks.

Phase 4: Multi-Agent Systems (1.5 months)

Learn Multi-Agent Architectures:

• Coordinator vs. Specialist agents

• Communication protocols (messages)

• Role-based agents (planner, executor, critic)

• Reflection loops (ReAct, Reflexion, etc.)

  
  

Frameworks:

• AutoGen (Microsoft)

• CrewAI (open-source)

• LangGraph (graph-based agent workflows)

• OpenDevin (AI software dev agent)

  
  

Build Projects:

• -Multi-agent task solver

• -AI content creation team (writer + editor + fact-checker agents)

• -AI recruiter (job-matching agent system)

  
  

Goal: Build autonomous, communicating AI systems with memory and tools.

Phase 5: Memory, Knowledge, and Context (1 month)

Learn About:

• Vector databases: Chroma, Pinecone, Weaviate, FAISS

• Context window management

• Episodic vs. semantic memory

• Knowledge graphs

  
  

Tools:

• LangChain Memory

• LlamaIndex Memory

• Postgres + pgvector

• Milvus / ChromaDB

Goal: Make your agent remember — past chats, tasks, or learn from new data dynamically.

Phase 6: Real-World Integration (2 months)

Integration Skills:

• RESTful APIs & webhooks

• Databases (Postgres, MongoDB)

• Frontend (React/Next.js or Streamlit)

• Cloud (AWS Lambda, GCP Functions)

  
  

Tools to Learn:

• FastAPI for backends

• Streamlit / Gradio for AI dashboards

• Docker + GitHub Actions for deployment

  
  

Example Projects:

• -AI Research Assistant

• -AI Email Manager

• -Autonomous Business Analyst

• -AI Developer Assistant (like Devin)

• -AI Workflow Orchestrator for marketing/data tasks

  
  

Phase 7: Advanced Concepts (Ongoing / 2–3 months)

Study Advanced Topics:

• Agentic reasoning (Reflexion, ReAct)

• Planning algorithms (Tree of Thoughts, Graph of Thoughts)

• Reinforcement Learning with Human Feedback (RLHF)

• Fine-tuning LLMs for agent tasks

• AI safety and alignment principles

Goal: Understand the science behind autonomous decision-making.

Phase 8: Projects & Portfolio

Build Real Projects:

1. Research Assistant Agent — reads PDFs, answers questions

2. AutoGPT Clone — multi-step goal completion

3. Financial Analyst Agent — pulls data, builds insights

4. Multi-Agent Chat System — debate or collaborate on a goal

5. Memory-Enabled Chatbot — remembers context from previous sessions

Phase 9: MLOps + AgentOps (Deployment Level)

• Learn LLMOps / AgentOps concepts

  • Tracing, evaluation, and debugging agents

  • Logging & monitoring (LangSmith, Helicone)

  • Feedback loops

  • Cost optimization

• Deploy agents to production

  • Dockerize

  • Deploy on AWS/GCP/Azure

  • Use LangServe / FastAPI endpoints

    
    

Suggested Timeline (8–10 Months)

Tech Stack Summary

Bonus: Learn from Real-World Projects

• OpenDevin (GitHub)

• AutoGPT / BabyAGI

• Voyager

• MetaGPT

• CrewAI official examples