How to Choose the Right Agentic Design Pattern
A practical decision framework for picking the right agentic pattern for your AI system — from prompt chaining to multi-agent.
Start with prompt chaining (simplest). Add reflection for quality. Add tool use for real-world interaction. Add routing for branching logic. Add parallelization for speed. Go multi-agent only when complexity demands it.
The golden rule
Start with the simplest pattern that solves your problem. Don't jump to multi-agent when prompt chaining will do. Don't add planning when a simple chain handles it. Every additional pattern adds latency, cost, and debugging complexity. Earn your complexity.
The decision ladder
Think of agentic patterns as a ladder of increasing complexity. Each rung solves a new class of problems. Rung 1: Prompt Chaining — 'I need to do things in sequence.' Rung 2: Reflection — 'I need better quality output.' Rung 3: Tool Use — 'I need to call external systems.' Rung 4: Routing — 'Different inputs need different handling.' Rung 5: Parallelization — 'I need to do things simultaneously.' Rung 6: Planning — 'The agent needs to figure out the steps itself.' Rung 7: Multi-Agent — 'No single agent can handle this.'
NEED → PATTERN
Sequential steps → Prompt Chaining
Better quality → + Reflection
External system interaction → + Tool Use
Input-dependent branching → + Routing
Speed from parallel tasks → + Parallelization
Complex goal decomposition → + Planning
Diverse expertise needed → Multi-Agent
Long-term context → + Memory Management
Up-to-date knowledge → + RAG
Universal tool connectivity → + MCPCommon combinations
In production, you rarely use one pattern alone. Common combos: (1) Prompt Chaining + Reflection — high-quality sequential processing (content generation, code writing). (2) Tool Use + Routing — agent that handles different request types with different tools (customer support). (3) RAG + Reflection — retrieval with fact-checking (knowledge assistants). (4) Planning + Tool Use + Memory — autonomous task completion (coding agents, research agents). (5) Multi-Agent + all of the above — complex workflows (software teams, research teams).
Start here
If you're building your first AI agent, start with Prompt Chaining. Break your task into 3–5 sequential steps. Get it working. Then ask: is the output quality good enough? If not, add Reflection. Does the agent need to call APIs? Add Tool Use. Is it too slow? Add Parallelization. Build incrementally. Every production agent I've seen started simple and grew patterns as needed.
Start with the simplest pattern (prompt chaining), layer on complexity only when needed. Most production AI agents use 2–3 patterns combined. Earn your complexity.