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Become an Agentic AI Engineer in 90 Days
Master AI Agents, Multi-Agent Systems, and Real-World Automation — One Week at a Time

by Ravi Yeluru

Want to hear the story behind this plan? Listen to the breakdown on Spotify​
This step-by-step plan takes you from basic AI concepts to building complex multi-agent systems. Each week builds on what you learned before, creating a smooth learning path for software developers wanting new skills or AI fans interested in how agents work.
Why You Can Do This
If you’ve got 3+ years of coding experience under your belt, you’re already equipped with everything you need — logic, APIs, problem-solving. The only thing missing? The AI layer: LLMs, agent frameworks, memory, vector databases. This plan helps you with those exact skills — step by step — so you can confidently design and deploy agent-powered systems from scratch.
What Makes This Plan Work
Each week has a clear focus and a hands-on project to match. You’ll start by building simple agents, then connect them into smarter systems, and finally create full, automated workflows. The best part? You’ll only learn the tools that matter — like LangChain, AutoGen, and LangGraph — no wasted time on fluff.
Time Needed
Ideally, set aside 2–3 focused hours per day. But if you’re working full-time, no worries — the plan is flexible. You can batch tasks on weekends, do hands-on projects on Saturdays, and keep Sundays light with reviews or catch-up. Since most projects are broken into 2–3 day chunks, staying consistent is more important than being perfect.
Tools & Prerequisites
Before you begin, make sure you have the following set up:
  • Python 3.9 or higher installed on your system
  • A basic understanding of APIs and web services
  • A GitHub account for version control and sharing your work
  • Free-tier accounts on OpenAI, Hugging Face, and Pinecone
If you’re missing any of these, check the “Week 0” section in the resources list for setup guidance.
Agentic AI Tech Stack
This tech stack covers the core building blocks of agentic AI — from reasoning engines to memory, workflows, and user interfaces. Each layer builds on the one before it, helping you go from simple agents to powerful, real-world systems. By the end of the journey, you’ll know how to combine these tools to solve real problems with intelligent, autonomous software.
LLM Layer
Foundation models like OpenAI, Anthropic, and Gemini power the thinking behind AI agents. They understand language, process context, and generate intelligent responses — allowing your agents to reason, make decisions, and communicate like a human assistant.
Memory Layer
Tools like FAISS, Chroma, and Pinecone act as memory for your agents — letting them store, recall, and use past information. This memory layer helps agents keep context across conversations, learn from earlier interactions, and pull in relevant facts without repeating the same work.
Agent Frameworks
LangChain, AutoGen, and CrewAI provide structured approaches to building and orchestrating AI agents. These frameworks handle the complex logic of agent behavior, tool use, and reasoning patterns with pre-built components that dramatically reduce development time and enable sophisticated capabilities without starting from scratch.
Monitoring & Workflow
Tools like LangSmith, LangGraph, n8n, Zapier, Prometheus, and Grafana help you monitor, debug, and optimize AI agents in the real world. They give you insight into how your agents think, allow you to trace mistakes, and support complex multi-step workflows. Good monitoring isn’t optional — it’s what keeps your agents reliable, scalable, and continually improving.
UI & Integration
Tools like Streamlit, Gradio, and React turn your AI agents into user-friendly apps that people can actually use. And with connectors like Zapier, n8n, and Make, your agents can talk to other tools, services, and workflows. APIs like FastAPI and Flask let you integrate everything cleanly — giving your agents both a face and a bridge to the real world.
Week 1: Introduction to AI Agents
Week 1 kicks things off by showing you what AI agents really are — smart software helpers that can think, plan, act, and remember. You’ll explore core concepts like autonomy and reasoning, and then build your first functional memory-based agent using LangChain — setting the foundation for everything that comes next.
Days 1–2: What is an AI Agent?
Explore the agent loop (observe-think-act), autonomy levels, and tool integration capabilities that define AI agents. Learn how agents perceive their environment, make decisions, and execute actions to achieve goals.
Days 3–4: Key Concepts
Master critical agent capabilities including planning strategies, prompt engineering techniques, protocols, and state management. Learn how these elements combine to create effective agent behavior.
Days 5–7: Build a simple memory-based agent
Implement your first functional agent with short-term memory capabilities. You'll create an agent that can maintain context across multiple interactions, learn from past exchanges, and progressively improve its responses.
Week 1 gives you your first hands-on experience with AI agents — from understanding how they think and act to building one that remembers. Courses like LangChain for LLM Development and AI Agents in LangGraph are a great companion, helping you connect concepts like memory and workflows to your first working agent.
Week 2: Memory & Vector Stores
Agents need memory to be truly intelligent — not just to respond, but to remember, learn, and adapt. In Week 2, you'll dive into short-term and long-term memory systems using vector databases like FAISS, Chroma, and Pinecone. By the end, you'll build a document-aware QA agent that remembers what it's read and answers questions with context — a key skill for real-world applications.
Days 1-2: Memory Types
Learn about different memory mechanisms that agents use to retain information: short-term memory for immediate context, long-term memory for persistent knowledge, and semantic memory for conceptual understanding. The LangChain Memory Guide provides comprehensive implementation patterns.
Days 3-4: Vector Databases
Explore vector database solutions for storing and efficiently retrieving embeddings: FAISS for local development, Chroma for simple persistence, and Pinecone for production-scale retrieval. Learn how to implement semantic search using vector similarity to find relevant information.
Days 5-7: QA Agent Project
Build a practical question-answering agent that combines vector stores with document loading and retrieval-augmented generation. Learn to implement the RetrievalQA pattern to create an agent that answers questions using knowledge from your documents.
This week gives your agents a memory upgrade. You’ll work with tools like FAISS, Chroma, and Pinecone to store and retrieve knowledge — and use it to build a document-aware QA agent. Courses on vector databases and semantic search support your learning with hands-on labs that bring retrieval-augmented generation (RAG) to life.
Week 3: Tool Use & Reasoning
This week is when your agents start to think before they act. You’ll explore the ReAct framework to combine reasoning with tool use, learn how to split planning from execution, and build an agent that can call real APIs in the middle of its decision-making process. These techniques let your agent go beyond simple responses — and actually solve complex, multi-step tasks.
ReAct Framework
Days 1-2: Learn the Reason + Act pattern for thinking and using tools
Study the ReAct framework that enables agents to reason about tasks before taking actions. This pattern combines reflection with tool use, allowing agents to plan their approach and adjust based on intermediate results. Implement basic reasoning chains with LangChain chains.
Planner-Executor
Days 3-4: Explore agents that separate planning from execution
Dive into planner-executor patterns that divide complex tasks into distinct planning and execution phases. Learn how this separation improves agent performance on multi-step tasks by creating explicit plans before executing actions. Practice with LangChain agent toolkits to implement this architecture.
API Tool Agent
Days 5-7: Build an agent that can invoke API tools within a reasoning loop
Create a practical agent that can interact with external API tools as part of its reasoning process. Implement tool-using agents that can make API calls, process the returned data, and incorporate the results into their reasoning. Apply this in a real-world scenario using agent executors.
You now know how to make agents that think step by step — and act with purpose. This week’s work on ReAct, planner-executor, and tool-based reasoning is supported by the Functions, Tools, and Agents with LangChain course. By the end, your agent won’t just respond — it will reason, plan, and use tools intelligently.
Week 4: Trip Planner Agent Mini-Capstone
Week 4 brings together everything from Phase 1 in a mini-capstone project. You'll build a Trip Planner Agent that combines prompts, memory, tools, and planning to help users plan travel itineraries, demonstrating how these components work together in a practical application.
Days 1-3: Implement Memory
Use FAISS or Chroma to remember places the user has searched. This creates a persistent memory system that allows your agent to recall and reference previous interactions, enabling more personalized recommendations based on user history.
Days 4-5: Add Travel APIs
Integrate flight and hotel APIs using RapidAPI for testing. Learn to implement custom tool interfaces that allow your agent to query external services for real-time data, making your trip planner capable of accessing up-to-date travel information.
Days 6-7: Add Reflection
Implement a reflection loop so the agent can improve its plans based on results. This advanced technique enables your agent to evaluate its own outputs, identify potential issues, and refine its recommendations through iterative self-improvement.
In this mini-capstone, you brought your agent to life — combining memory, tools, and planning into one seamless system. It remembers your past inputs, pulls live travel data, and improves its suggestions with reflection. This project proves how far you’ve come — and sets the stage for building more intelligent, real-world agents.
Week 5: Multi-Agent Collaboration
This week, you’ll explore what happens when agents stop working alone — and start working together. Multi-agent collaboration unlocks powerful new use cases, from brainstorming ideas to dividing up tasks. You’ll build role-based agents that message each other, coordinate efforts, and solve problems as a team — starting with a collaborative essay generator.
Days 1-2: Single vs Multi-Agent Systems
Learn the fundamentals of multi-agent systems using AutoGen and CrewAI, understanding how agents message and coordinate with each other. Explore LangChain's multi-agent patterns for implementing effective agent-to-agent communication protocols.
Days 3-4: Role-Based Agents
Build specialized agents with different roles (Researcher, Writer, Checker) that can access different tools and memories to collaborate on tasks. Implement supervisor agents to coordinate teamwork and custom tools for each role's specialized capabilities.
Days 5-7: Essay Generator Project
Create a system with 3 agents that work together to research, write, and check an essay, practicing message passing and tool use in a team format. Apply agent simulation techniques and implement shared memory systems so agents can build on each other's work.
This week strengthens your understanding of multi-agent systems through hands-on collaboration. The Multi AI Agent Systems with CrewAI course brings this to life with real projects like resume tailoring and event planning. For deeper experimentation, explore the AutoGen GitHub repo — packed with role-based agent examples.
Week 6: Workflow + Planning with LangGraph
Now that your agents can think and collaborate, it’s time to give them structure. In Week 6, you’ll learn to design multi-step workflows with LangGraph — including branches, loops, and conditional logic. You’ll also customize agent behaviors using AutoGen, then put it all together in a "Startup Builder" simulation where agents take on real-world roles to plan and execute a project.
LangGraph Basics (Days 1-3)
Learn to create graphs of agents that visualize step-by-step flows and decision processes. You'll master LangGraph fundamentals for building state machines that guide agent decision making, implement conditional paths, and design feedback loops for complex reasoning tasks.
AutoGen Custom Agents (Days 4-5)
Explore how to run multi-step conversations in loops with customized AutoGen agents. You'll learn to implement agent workflows with memory persistence, orchestrate conversations between multiple specialized agents, and create dynamic reasoning paths based on intermediate results.
"Startup Builder" Project (Days 6-7)
Build a multi-agent application with Founder, Engineer, and Designer roles that simulates startup brainstorming and planning. This project combines role specialization with workflow management to create a system where agents collaborate on business planning through structured stages from ideation to implementation planning.
This week brings structure to your agent systems. The AI Agents in LangGraph and Agentic Design Patterns with AutoGen courses are great companions — covering state machines, conditional logic, and multi-agent coordination. For extra depth, dive into the LangGraph docs and build on your “Startup Builder” project.
Week 7: Memory + Reflection + Logging
Intelligent agents don’t just act — they reflect, learn, and improve. In Week 7, you’ll add long-term thinking to your agents with episodic memory and self-reflection techniques. You’ll also track their internal reasoning using observability tools like LangSmith. To bring it all together, you’ll build a resume critique agent that offers feedback, remembers its suggestions, and gets better with each interaction.
Days 1-2: Episodic Memory
Learn to implement task-level memory using LangChain Episodic Memory to help agents recall past interactions and build context-aware responses. You'll explore how to store, retrieve, and utilize past experiences to improve reasoning.
Days 3-4: Logging & Observability
Use LangSmith to debug how agents make decisions and track their reasoning. You'll implement systems to monitor agent performance, identify bottlenecks, and create feedback mechanisms that help agents learn from their successes and failures.
Days 5-7: Resume Critique Project
Build an agent that can critique resumes with memory, feedback loops, and tool use. You'll integrate self-reflection capabilities and demonstrate how agents can provide iterative feedback while remembering previous suggestions and improvements.
You now have the tools to build agents that remember, reflect, and improve over time. The LangGraph memory courses and AutoGen design patterns will take you deeper into these concepts, and if you're curious, academic papers on agent reflection offer fascinating insights for the future.
Real-World Integrations & Expert Systems
Weeks 8-10 is all about connecting your agents to the outside world. You’ll learn how to integrate tools like email, web search, document readers, and databases — so your agents can interact with real systems, not just chat. From sending emails to pulling data through APIs, this week takes your agents from isolated scripts to full-fledged assistants.
Week 8: API, Email, Tools Integration
Connect agents to external software tools and services, enabling them to interact with real-world applications. Learn to implement API integrations, access email systems, and leverage tool-using agents that can search the web, process documents, and interact with databases.
Week 9: Workflows & Automation
Link agents to full workflows using platforms like Zapier or n8n, creating end-to-end automated solutions. Master techniques for chaining outputs between systems, implementing monitoring, and building resilient error handling for production deployments.
Week 10: Capstone & Final Portfolio
Build a complete AI system with user interface that demonstrates all the skills acquired throughout the course. Apply question answering, summarization, prompt engineering, and LLM integration techniques to create a production-ready solution that solves a real business problem.
By the end of this period, you’ll have a working system that connects to real-world tools, runs automated workflows, and demonstrates your skills through a polished, portfolio-ready AI project.
Learning Resources
Here are the key resources you'll need to learn about AI agents, core concepts, and how to build memory systems with LangChain. Follow this numbered sequence for optimal learning:
Prerequisites (Week 0)
Check your technical skills first such as Python, APIs, LLM basics, Prompt Engineering. If needed, use resources like Codecademy to improve your Python knowledge.
Course Sequence:
Mini-Capstone (Week 4)
Use what you've learned to build a working TripPlanner Agent
Project Steps:
  1. Apply agent design patterns from previous courses
  1. Implement memory systems and tool usage
  1. Share your project on GitHub and LinkedIn to grow your network and get noticed
Advanced Techniques (Weeks 5-7)
Learn how agents work together, manage workflows, and track progress with LangSmith. Read research papers to better understand how reflection works.
Course Sequence:
Integration & Production (Weeks 8-10)
Learn how to connect APIs, set up workflows, and build real-world AI systems that are ready for production.
Course Sequence:
  1. Final Capstone Project: Integrate all skills into a production-ready system
AI agent systems are built in layers: first with language models that can reason, then adding memory, behavior frameworks, workflow tools, and finally, easy-to-use interfaces. Each part builds on the last, creating more powerful AI solutions.
Practice what you learn by building projects – the TripPlanner Agent in Week 4 and your final project in Week 10. Boost your profile by sharing these on GitHub and LinkedIn to connect with others in the field.
Good Luck on Your Agentic AI Engineering Journey!