AI Agent Development with LangGraph & AutoGen (2026)
Build production-ready multi-agent systems with tool calling, persistent memory, and LangGraph state machine workflows in 4 weeks
Last updated: April 2026 • 7,600+ students enrolled
Key Takeaways — What you will build in 4 weeks:
- Understand the agent loop — Observe → Think → Act — and build it from scratch
- Implement ReAct (Reasoning + Acting) agents that reason about which tools to call
- Build LangGraph state machines with nodes, edges, conditional routing, and loops
- Create tool-using agents that can search the web, run Python code, and query databases
- Add persistent memory so agents remember past conversations and decisions
- Build a multi-agent AutoGen system where specialist agents collaborate on complex tasks
- Evaluate agent performance — benchmark task completion and tool use accuracy
What You’ll Learn
ReAct Agent Pattern
LangGraph State Machines
Tool Calling & Function Calling
AutoGen Multi-Agent Systems
Agent Memory (Short & Long Term)
Web Search Tool Integration
Agent Evaluation & Benchmarking
Production Agent Deployment
Full Curriculum — 4 Weeks, 20 Lessons
Week 1 — Agent Fundamentals & Tool CallingWeek 1
Lesson 1: What are AI agents? Agent vs chatbot vs automation
Lesson 2: The ReAct pattern — Thought, Action, Observation loop
Lesson 3: OpenAI function calling — giving LLMs access to tools
Lesson 4: Building custom tools — web search, code execution, database queries
Project 1: Research Agent — give LLM tools to search the web and summarize findings
Week 2 — LangGraph State Machine WorkflowsWeek 2
Lesson 5: LangGraph fundamentals — StateGraph, nodes, edges, state schema
Lesson 6: Conditional routing — agents that decide their own next step
Lesson 7: Loops and iteration — agents that retry failed actions
Lesson 8: Human-in-the-loop — pause the agent for human approval of critical actions
Project 2: Autonomous Code Assistant — agent that writes, tests, and fixes Python code
Week 3 — Multi-Agent Systems with AutoGenWeek 3
Lesson 9: AutoGen fundamentals — AssistantAgent, UserProxyAgent, GroupChat
Lesson 10: Specialist agents — planner, executor, critic, summarizer agents
Lesson 11: Agent communication patterns — sequential, parallel, hierarchical
Lesson 12: Combining LangGraph + AutoGen in production systems
Project 3: Multi-Agent Data Analysis — planner, SQL writer, analyst, and reporter agents working together
Week 4 — Memory, Evaluation & Production DeploymentWeek 4
Lesson 13: Short-term memory — conversation context within a session
Lesson 14: Long-term memory — persistent memory across sessions with vector stores
Lesson 15: Agent evaluation — task completion rate, tool call accuracy, latency
Lesson 16: Deploying agents with FastAPI — expose your agent as a REST API
Project 4: Personal AI Assistant — persistent memory, tool use, multi-step task completion
Prerequisites
- Python — comfortable with OOP, decorators, async basics
- LangChain basics — recommended to complete Course 01 (Prompt Engineering) or Course 03 (RAG) first
- OpenAI API account with function calling access
- Basic understanding of REST APIs and JSON
Career Outcomes & Salaries
AI Agent Engineer
₹15–30 LPA
Design and build autonomous AI agents that complete multi-step tasks with minimal human intervention
Agentic Systems Developer
₹14–28 LPA
Build multi-agent pipelines for enterprise automation — legal, finance, customer service, and operations
AI Automation Architect
₹25–50 LPA
Design end-to-end agentic AI systems that replace manual workflows at large enterprises
LLM Platform Engineer
₹18–35 LPA
Build internal AI platforms that enable teams to deploy agents with monitoring and governance
What Students Say
★★★★★
“LangGraph clicked for me in Week 2. The state machine approach is so much cleaner than trying to do complex agent logic with plain LangChain. Project 2 (code assistant) is genuinely impressive.”
Nikhil Joshi
Senior Developer, ThoughtWorks AI Practice
★★★★★
“The multi-agent section with AutoGen is the best content I’ve seen on this topic. Building the planner+executor+critic pattern in Project 3 was mind-blowing. This is what production AI looks like.”
Sneha Agarwal
ML Engineer, Razorpay AI Team
★★★★☆
“Human-in-the-loop lesson was a revelation. Now I understand how to build agents that are safe to deploy without worrying about runaway actions. Essential knowledge for enterprise AI.”
Rohan Deshmukh
AI Consultant, Deloitte India
Frequently Asked Questions
What is an AI agent and how is it different from a regular LLM application?
A regular LLM app follows a fixed prompt-response flow. An AI agent can reason, choose tools, loop, and adapt its plan based on results. LangGraph makes this controllable with a state machine graph — nodes are actions, edges are decisions. Agents are the future of enterprise AI automation.
What is LangGraph and why use it instead of plain LangChain for agents?
LangGraph uses a directed graph model — nodes are functions, edges are flow decisions. This allows conditional routing, loops, and parallel execution that are impossible in plain LangChain chains. Since 2025, LangGraph is the industry standard for production agentic applications.
What is the difference between LangGraph and AutoGen?
LangGraph: fine-grained control over a single agent’s state machine. Best for complex single-agent workflows. AutoGen: multi-agent conversation framework where specialized agents collaborate. Best for team-of-agents patterns. This course teaches both and when to use each in production.
What are the best AI agent jobs in India in 2026?
Top roles: AI Agent Engineer (₹15–30 LPA), Agentic Systems Developer (₹14–28 LPA), AI Automation Architect (₹25–50 LPA). These roles are growing fastest at AI-first startups and global product companies with India engineering centers.
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