Home/Agentic AI/Graph Workflows/Introduction
←
Previous Module
State Machines for Agents

Graph-Based Workflows

Build scalable multi-agent systems with directed acyclic graphs

Your Progress

0 / 5 completed
1
2
3
4
5

What Are Graph-Based Workflows?

Graph-based workflows represent agent tasks as nodes and their dependencies as edges, forming Directed Acyclic Graphs (DAGs). This approach enables parallel execution, conditional branching, and complex orchestration patterns.

Why Graphs for Agent Workflows?

⚡

Parallelization

Execute independent tasks simultaneously

🔀

Flexibility

Dynamic routing based on results

📊

Clarity

Visual representation of dependencies

Interactive: Explore a Simple Graph

Click nodes to explore this basic workflow graph. Notice how Task A must complete before B and C can start.

SELECTED NODE:
Click a node to see details

Core Components

Nodes
Individual tasks or agents in your workflow
Edges
Dependencies showing which tasks must complete first
DAG Property
No cycles - guarantees workflows terminate

Key Advantages

→
Automatic Parallelization
Independent branches execute concurrently
→
Clear Dependencies
Visual representation prevents confusion
→
Dynamic Routing
Conditional paths based on results

💡 Key Insight

Graphs transform complex workflows into manageable structures. By explicitly modeling dependencies, you gain automatic parallelization, clear execution order, and the ability to optimize critical paths. What seems like simple nodes and edges unlocks sophisticated orchestration patterns.