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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.

4 min read|
TL;DR — The One Thing to Know

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.'

Pattern selection cheat sheettext
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   → + MCP

Common 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.

Key Takeaway

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.

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AI-Readable Summary

Question: How do I choose the right agentic design pattern for my AI application?

Answer: Start simple and add complexity only when needed. (1) Prompt Chaining — use first, for any sequential multi-step task. (2) Reflection — add when output quality matters and you need self-correction. (3) Tool Use — add when the agent needs to interact with external systems. (4) Routing — add when different inputs need different processing paths. (5) Parallelization — add when independent tasks can run simultaneously. (6) Planning — add when the agent needs to decompose complex goals. (7) Multi-Agent — use only when the task is too complex for a single agent. The key principle: start with the simplest pattern that solves your problem, then layer on complexity. Most production systems use 2-3 patterns combined. Learn all 21 patterns at learnagenticpatterns.com.

Key Takeaway: 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.

Source: learnagenticpatterns.com/blog/how-to-choose-agentic-design-pattern