AI Agents Design Patterns Explained
A comprehensive overview of the major design patterns for building AI agents, from single-agent architectures to multi-agent collaboration, with clear diagrams and practical trade-offs for each pattern.
Kerem Aydin ·
Overview
Kerem Aydin presents a pattern catalog for AI agent architectures, providing a structured way to think about how agents are composed and coordinated. The article bridges the gap between theoretical agent frameworks and practical implementation choices.
Core Patterns
Single Agent Patterns
- ReAct (Reasoning + Acting) — the agent alternates between reasoning about the current state and taking actions. This is the foundational loop: Think → Act → Observe → Repeat. Most basic agent implementations follow this pattern.
- Tool Use Agent — extends the ReAct loop with an explicit tool selection step. The agent decides which tool to invoke based on the current task, executes it, and incorporates the result.
Multi-Agent Patterns
- Supervisor Pattern — a central supervisor agent delegates tasks to specialized worker agents and synthesizes their results. Clear hierarchy, easy to reason about, but the supervisor can become a bottleneck.
- Hierarchical Pattern — extends the supervisor model with multiple layers of management. Useful for very complex tasks that need recursive decomposition.
- Collaborative (Peer) Pattern — agents communicate as equals, passing messages to coordinate without a central controller. More flexible but harder to debug and predict.
- Pipeline Pattern — agents are arranged in a sequential chain, where each agent’s output feeds into the next. Best for well-defined, linear workflows.
Choosing a Pattern
The article emphasizes that pattern choice depends on:
- Task complexity — simple tasks rarely need multi-agent setups
- Reliability requirements — supervisory patterns offer more control and predictability
- Latency tolerance — more agents mean more LLM calls and higher latency
- Debuggability — hierarchical and pipeline patterns are easier to trace than peer-to-peer
Key Takeaway
Start with a single ReAct agent. Add complexity (multi-agent, hierarchy) only when you hit clear limitations. The best agent architecture is the simplest one that reliably solves your problem.