A comprehensive guide to AI agent architecture patterns for production systems. Covers 10 distinct architectural patterns — from single calls and chains to supervisor agents and human-in-the-loop — with JavaScript code examples for each. Explains how to select models by task type (frontier vs. small/cheap), the trade-offs of each pattern, and how to combine multiple architectures in a real travel booking agent. Also addresses why a single powerful model call is insufficient at scale, citing cost, observability, seams for code intervention, and optimization flexibility as key reasons for multi-agent pipelines.
Table of contents
What an AI agent is 🔗Picking a model 🔗Architecture types 🔗Picking one architecture 🔗Our architecture for the vacation agent 🔗Why not one call to one powerful model? 🔗References 🔗Questions this post answers
What are the main architecture patterns for building AI agents in production?
Ten patterns cover most production agent needs: single call, chain, router, fan-out and voting, generator plus reviewer, tool loop, plan-then-execute, supervisor, handoff, and human-in-the-loop. Each fits a different situation — use a tool loop when the next step depends on what the model finds, a supervisor when you need named specialists with one accountable place, and human-in-the-loop for any irreversible action. Engineers designing multi-agent systems track new patterns and real-world trade-offs on daily.dev.
Why use a multi-agent pipeline instead of one powerful frontier model for a complex AI task?
A multi-agent pipeline beats a single frontier model on four dimensions at scale: cost (small models handle classification, extraction, and email at 5–10x lower token rates), seams (plain code can intercept between stages for exact arithmetic the model gets wrong), visibility (each stage leaves an artifact so failures are traceable), and optimization (each job can be independently downgraded or fine-tuned without affecting others). Developers choosing between single-model and pipeline approaches find real trade-off discussions on daily.dev.
What are the failure modes of a generator plus reviewer architecture for AI agents?
Three failure modes are common: a reviewer whose prompt tells it to approve everything becomes a rubber stamp; a reviewer judging a property it was never shown examples of produces meaningless verdicts; and a reviewer drawn from the same model family as the writer shares the writer's blind spots, waving through the exact mistakes that family makes most often — called same-family bias. Teams building review loops for AI output stay ahead of these pitfalls on daily.dev.