Agentic AI Design: Why Workflow & Agent Patterns Matter
Agentic AI is changing the game but success is not just about using LLMs. It is about orchestrating them with the right patterns. Frameworks like LangChain and LangGraph make this possible, but understanding these patterns is critical for building scalable, reliable AI systems.
Here are 6 basic foundational patterns every AI practitioner should know:
✅ Prompt Chaining
Break complex tasks into smaller steps.
Example: Summarize → Extract insights → Recommend actions.
Use when: Multi-step reasoning is needed.
✅ Parallelization
Run tasks concurrently for speed.
Example: Fetch weather, stock prices, and news simultaneously.
Use when: Tasks are independent and need better performance.
✅ Orchestrator-Worker
One agent plans, others execute.
Example: Orchestrator assigns marketing copy tasks to sub-agents for tone, SEO, visuals.
Use when: Complex workflows need coordination.
✅ Evaluator-Optimizer
One agent critiques, another improves.
Example: Draft blog → Evaluator checks tone → Optimizer refines.
Use when: This is a good candidate for Quality-sensitive outputs.
✅ Routing
Direct queries to the right chain or agent.
Example: “Weather in London?” → Weather agent; “Stock price?” → Finance agent.
Use when: Useful in Multi-domain apps.
✅ Agent Pattern
Autonomous agents with memory and tools.
Example: A travel agent that books flights, hotels, and suggests itineraries.
Use when: Tasks need autonomy and adaptability.
💡 Tip: Start simple with prompt chaining, then layer in routing and orchestration as complexity grows.
Here summarizing the above foundational patterns for clarity:
Prompt Chaining → Sequential steps
Parallelization → Concurrent tasks
Orchestrator-Worker → Supervisor delegating tasks
Evaluator-Optimizer → Critique and improve
Routing → Direct queries to correct chain
Agent Pattern → Autonomous agent with memory and tools
Tip: It’s essential to learn these foundational patterns first before moving to advanced patterns like ReAct, Reflexion, and Tree-of-Thought, which enable deeper reasoning and adaptability.
