LangGraph Masterclass 2026: OpenAI Agents, Chains, MCP, A2A

Learn why a plain LLM can't take actions on its own, and how adding tools and a Think → Act → Observe loop turns it into an agent.

- Explain what an AI agent is in plain English — and why it's different from a regular chatbot or a simple API call

- Describe the think → act → observe loop that every agent runs, and trace through a real example step by step

- Identify the three things every agent needs: a brain (LLM), tools, and a reasoning loop

- Recognize agent behavior when you see it in the wild — in products, demos, and job descriptions

Learn how to connect a prompt, an LLM, and an output parser into a clean reusable pipeline using LangChain's pipe operator.

- Explain what a chain is and why it's the foundation every agent is built on

- Use the pipe operator (|) to connect a prompt, an LLM, and a parser into a working pipeline

- Trace exactly what data type flows between each step — dict, messages, AIMessage, string

- Force an LLM to return structured data (JSON) instead of free-form text, and use that data in code

- Connect two chains together so the output of one feeds into the next

Learn how the ReAct pattern gives an agent the ability to reason about which tools to call and keep looping until it has a complete answer.

- Explain the ReAct pattern (Reason + Act) and how it gives an agent the ability to make decisions

- Build a working ReAct agent in LangChain that can choose from a set of tools to answer a question

- Define custom tools using the @tool decorator and connect them to an agent

- Read an agent's reasoning trace and understand why it picked each tool at each step

- Explain what makes an agent different from a chain — and when to use each

Learn how splitting a task into a structured plan before executing it helps agents handle complex, multi-step questions without losing track.

- Understand the basics of States, Notes, and Edges from LangGraph

- Understand the basics of States, Notes, and Edges from LangGraph

- Explain the Plan and Execute pattern and why it handles complex, multi-step tasks better than ReAct alone

- Describe the difference between the planner (decides what to do) and the executor (does it)

- Build a Plan and Execute agent using LangGraph and walk through how it breaks a problem into steps

- Identify real-world scenarios where Plan and Execute outperforms a standard ReAct loop

Learn how adding a self-evaluation loop — generate, score, retry — produces better agent outputs than a single-pass approach.

- Explain what Reflexion is and how self-critique improves an agent's output over multiple attempts

- Describe the generate → reflect → revise loop and why it produces better results than a single pass

- Build a Reflexion agent that evaluates its own answer and rewrites it when it falls short

- Recognize when Reflexion is worth the extra LLM calls — and when it isn't

Learn how splitting one overloaded agent into specialized agents with a supervisor router improves accuracy and reliability.

- Explain why large, complex tasks benefit from multiple specialized agents working together

- Describe the supervisor pattern — one agent that delegates subtasks to specialized worker agents

- Build a multi-agent system where agents hand off tasks to each other

- Identify the tradeoffs between a single powerful agent and a team of specialized agents

Learn how the A2A protocol lets agents advertise their capabilities and route tasks to each other dynamically without hardcoded wiring.

- Explain what the A2A (Agent-to-Agent) protocol is and the problem it solves from scratch

- Describe how agents discover and communicate with other agents they've never been configured for

- Build an agent that can delegate a task to another agent using the A2A protocol

- Understand how Python-A2A wrapper simplifies module building compared to building from scratch.

Learn how MCP eliminates integration fragmentation by giving every framework and every tool provider one universal standard to connect through.

- Explain what MCP is and the two problems it solves: framework lock-in and integration fragmentation

- Describe the difference between an MCP server (exposes tools) and an MCP client (discovers and calls them)

- Connect a LangChain agent to a local MCP server and have it automatically discover available tools

- Explain what happens under the hood when load_mcp_tools connects to a server

- Deploy an MCP server on Snowflake with a single SQL command and connect to it via URL

- Distinguish between building your own private MCP server and calling servers hosted by other providers

- Understand what MCP patterns look like in enterprise production settings.

- Understand what MCP patterns look like in enterprise production settings (Snowflake example)