Building Your First Agent with LangGraph
A Beginner-Friendly Introduction to Agentic AI Development
A hands-on, no-prior-experience-needed walkthrough of building your first AI agent using LangGraph — for anyone curious about agentic AI development.
About This Webinar
Agentic AI is moving fast, and LangGraph has become one of the most widely used frameworks for building AI agents that can reason, use tools, and make decisions — not just answer questions.
This session is built for absolute beginners. We start with a plain-language explanation of what an “agent” actually is, walk through how LangGraph structures one using state, nodes, and edges, and then build a working agent live, step by step.
No prior LangChain or agentic AI experience is needed — just basic familiarity with Python.
What You’ll Walk Away With
- A clear mental model — What an “agent” is, and how it differs from a chatbot or a simple LLM call.
- Understanding of LangGraph’s core building blocks — State, nodes, edges, and conditional routing.
- Hands-on exposure — Watch a real agent get built from scratch, live.
- Tool use basics — How to give your agent abilities like search or calculation.
- Common beginner pitfalls — And how to avoid them.
- A clear next step — Where to go after this session to keep building.
Who Should Attend
Developers New to Agentic AI
Curious about LangGraph and building agents, with no prior experience needed.
Python Developers
Comfortable with basic Python who want a practical entry point into agent development.
AI/ML Enthusiasts
Wanting a hands-on, beginner-friendly starting point rather than dense documentation.
ChatGPT/Claude Power Users
Who want to move from using AI tools to building something that acts, not just answers.
Basic Python familiarity is helpful. No prior AI/LangChain experience required.
90-Minute Agenda
- Quick intros and session roadmap
- Plain-language definition: agent vs. chatbot vs. simple LLM call
- Why agents matter right now
- What LangGraph is and how it relates to LangChain
- Core idea: agents as graphs — nodes, edges, state
- Why a graph-based approach beats a simple prompt chain for real tasks
- State: what it is, why it matters
- Nodes: functions that do the work
- Edges & conditional routing: how the agent “decides” what to do next
- Tools: giving your agent abilities (search, calculator, API calls)
- Setting up the environment (packages, API key)
- Defining state and a simple node
- Adding a tool and conditional edge
- Running the agent and watching it reason step-by-step
- Infinite loops, unclear state, over-complicating the first graph
- Debugging tips for when the agent doesn’t behave as expected
- Where to go next (docs, practice projects)
- Live Q&A
Meet Your Facilitator
Frequently Asked Questions
Seats Are Limited
Reserve your spot for this free, beginner-friendly session on building your first AI agent with LangGraph.