Building Your First Agent with LangGraph
A Beginner-Friendly Introduction to Agentic AI Development
Move beyond simply using AI. Learn how an AI agent works, how LangGraph structures agent workflows using state, nodes and edges, and see a working agent built live from scratch.
About This Webinar
LangGraph provides a structured way to build stateful, multi-step AI agent workflows. Instead of simply sending a prompt to an LLM and receiving an answer, an agent can work through a sequence of steps, use tools, and route the workflow based on what happens next.
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.
From Asking AI to Building an AI Agent
A chatbot typically responds to a request. An AI agent can work toward a goal through multiple steps, use available tools, observe results, and continue through a defined workflow. LangGraph helps developers structure that workflow.
What Makes an AI Agent Different?
Responds
Primarily responds to a user’s prompt or question.
Uses a Model
Places a language model inside an application workflow.
Acts Toward a Goal
Can choose actions, use tools and work through multiple steps.
Controls the Process
Connects decisions, actions and state into a structured workflow.
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 This LangGraph Webinar?
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.
The Four Building Blocks of LangGraph
What the Agent Knows
Information the workflow needs to maintain as the agent moves through its steps.
What the Agent Does
Functions or actions that perform work inside the agent workflow.
How the Workflow Moves
Connections that determine which step follows another step.
What Happens Next
Logic that allows the workflow to choose a path based on the current state or result.
Build Your First AI Agent — LIVE
The strongest part of the session is the live build. Follow the trainer as a working LangGraph agent is created step by step, from defining state to adding a tool and conditional routing.
30-Minute Live Build
See the workflow come together rather than only hearing about agent concepts.
LangChain vs LangGraph
If you are coming from the LangChain webinar, this is the natural next step: from building LLM applications to structuring more controlled agent workflows.
| LangChain | LangGraph |
|---|---|
| Provides building blocks for LLM-powered applications and agents. | Provides graph-based orchestration for custom, stateful agent workflows. |
| Useful for connecting models, prompts, tools and application logic. | Useful when you need explicit control over state, workflow steps and routing. |
| Can help you get started with LLM application development. | Helps structure multi-step agent behaviour and workflow execution. |
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
Where Can AI Agents Be Used?
Research Assistants
Agents that can gather information and work through multi-step research tasks.
Customer Support Agents
Workflows that can interpret requests, use tools and route actions.
Internal AI Assistants
Assistants designed around organisational knowledge and business workflows.
Business Workflows
Multi-step processes where an AI system needs to make decisions and take actions.
Your First Agent Doesn’t Have to Be Complicated
Beginners often make their first agent harder than it needs to be. This session keeps the starting point focused and addresses common issues such as:
- Infinite loops and workflows that never reach an end state
- Unclear or poorly defined state
- Adding too many nodes or tools too early
- Debugging when the agent does not behave as expected
Meet Your Facilitator
Want Your Teams to Build AI Agents?
A free webinar can introduce the fundamentals. Structured corporate training can help development and technology teams go further — from Generative AI and LangChain to LangGraph, AI agents and enterprise agentic workflows.
Frequently Asked Questions About LangGraph
Ready to Build Your First AI Agent?
Learn the fundamentals of LangGraph, see an agent built live, and understand where to go next with agentic AI development.