9 Agentic Harness Architectures Every AI Developer Must Know
Last Updated on August 19, 2026 by Editorial Team
Author(s): Divy Yadav
Originally published on Towards AI.
There are nine ways to wire an AI agent together, explained in plain English, with visuals.
Every AI agent you’ve ever used runs on one of nine wiring diagrams.

The article explains that AI developers often don’t lack a single “best” agent architecture—they usually struggle with choosing the right pattern for the problem. After outlining how workflows (preplanned paths controlled by code) differ from agents (paths decided by the model), it walks through nine fundamental architectures—starting with ReAct and Reflexion, then prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer, graph orchestration, and swarm—highlighting what each does, when it works, when it fails, and what it’s best for. It also emphasizes that real production systems typically combine a few patterns, encourages starting with the simplest setup and adding complexity only when metrics show a need, and notes key references behind the patterns.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.