Common Architecture Patterns

Beyond framework choice, understanding architecture patterns determines how you structure agent workflows. Most frameworks support multiple patterns—chains for simplicity, agents for flexibility, multi-agent for collaboration, graphs for complex logic.

Interactive: Pattern Visualizer

🔗 Sequential Chains

Linear sequence of steps where each output feeds into the next

Supported By

LangChainSemantic Kernel

Best Use Case

Simple workflows: research → write → edit

Flow Diagram

Input

→

Step 1

→

Step 2

→

Step 3

→

Output

🎯 When to Use Each Pattern

🔗

Sequential Chains: Start here for simple, predictable workflows. Fast, debuggable, and sufficient for 60% of use cases. Avoid for tasks needing dynamic tool selection.

🤖

ReAct Agents: When you need flexibility and tool selection. More powerful than chains but slower and less predictable. Best for research, analysis, multi-step reasoning.

👥

Multi-Agent Teams: For complex tasks benefiting from specialization. Higher overhead but better quality on intricate projects. Use when single agent hits quality ceiling.

📊

Graph Workflows: When you need conditionals, parallel execution, or loops. Most complex but most powerful. Overkill for simple linear workflows.

💡 Architecture Best Practices

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Start simple: Begin with chains, only add complexity when needed

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Measure latency: Agents add 2-5x latency vs chains—budget accordingly

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Test incrementally: Prototype pattern in isolation before full integration

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Mix patterns: Use chains for deterministic steps, agents for dynamic ones

Agent Framework Landscape

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Common Architecture Patterns

Interactive: Pattern Visualizer

🔗 Sequential Chains

🎯 When to Use Each Pattern

💡 Architecture Best Practices