Agentic AI Session2: Agentic AI Architecture using LangGraph Multi-Agent Systems

Summary: In Session 2 of our Agentic AI curriculum, we move beyond simple reasoning loops and explore how LangGraph enables structured, stateful, and production-ready multi-agent systems.

Want to learn by running a Multi-Agent system? Run the Agentic AI Session 2 notebook in Google Colab.

In Session 1, we learned how to make an AI think using the ReAct loop. That was a major milestone. But thinking alone is not enough.

When you step into the world of Multi-Agent Systems, structure becomes important. Without architecture, agents quickly turn into tangled scripts that collapse under production pressure.

In Session 2, we move from simple Python loops to a structured architectural system using LangGraph.

1. Why LangGraph? Moving Beyond the Loop

In the first session, a basic Python for loop was enough to drive reasoning. It worked well for small experiments.

But complexity can grow fast. The moment multiple agents must collaborate, retry, or self-correct, simple loops become fragile.

You need LangGraph when:

• You have cycles: Agent A calls Agent B, which may call Agent A again. Linear loops cannot manage this cleanly.
• You need checkpointing: If step 9 fails in a 10-step workflow, you should resume from step 9, not restart everything.
• You need visualization: If you cannot see the execution flow, debugging becomes guesswork.

LangChain provides components. LangGraph provides structure. Think of LangChain as a toolbox and LangGraph as the architectural blueprint.

2. Understanding LangGraph Through a Factory Analogy

Instead of thinking about code, imagine a factory assembling custom bicycles.

The State: The Clipboard

In normal scripts, data disappears when execution stops. In LangGraph, the State is persistent. Think of it as a clipboard attached to the product as it moves through the factory.

• It defines the schema, such as customer name, order type, and current status.
• Every node reads from the clipboard and writes updates back to it.
• Because state exists independently, you can pause, inspect, resume, or even roll back.

The Node: The Workstation

A Node represents a workstation. In code, it is simply a function. It reads the state, performs an action, updates the state, and returns control.

The Edge: The Conveyor Belt

Edges determine how work flows between nodes.

• Standard edge: A direct path from one node to the next.
• Conditional edge: Logic that decides what happens next based on state.

Conditional edges enable self-correction. For example, if validation fails, the process loops back instead of crashing.

Component	Engineering Definition
State	A shared schema that every node reads and updates.
Node	A function that performs logic and returns state changes.
Edge	Connections that define execution paths.

If you want any of the following, send a message using the Contact Us (left pane) or message Inder P Singh (7 years' experience in AI and ML) in LinkedIn at https://www.linkedin.com/in/inderpsingh/

• Production-grade Agentic AI templates with playbooks
• Working Agentic AI projects for your portfolio
• Deep-dive hands-on Agentic AI training
• Agentic AI resume updates

3. DAG vs Stateful Graphs: Where True Agency Emerges

LangGraph supports two major execution patterns.

Directed Acyclic Graph (DAG)

A DAG flows forward only. There are no loops. This is predictable and easy to test, but it limits agent autonomy.

Stateful Graph

A stateful graph allows cycles. Agents can think, act, observe, and repeat until a task is complete. This is where real agentic behavior begins.

Cycles allow correction, refinement, and iterative reasoning. That is the power gap between simple automation and intelligent systems.

4. Engineering for Production: Abstraction and Determinism

Professional Agentic AI systems never hardcode a single model.

Model Abstraction

The graph should not care whether the reasoning engine is GPT-4o, Gemini, or a local Llama model. By abstracting model configuration, you can switch providers at runtime.

This layer also handles retries, rate limits, and fallbacks automatically.

Determinism and Reliability

Large language models are inherently probabilistic. In production, that unpredictability must be controlled.

• Temperature set to 0: Minimizes randomness and increases consistency.
• Fixed seed values: Ensures repeatable sampling across executions.

These controls transform AI from experimental to dependable.

The Bottom Line

Session 2 was about evolution. We moved from a script that calls an AI to an architectural system that manages state, handles retries, supports cycles, and scales safely.

Agentic AI is not about calling a model repeatedly. It is about designing structured reasoning systems that behave predictably in production.