Multi-agent AI is the next frontier. Learn how to coordinate specialist AI agents — researchers, writers, coders, reviewers — working together on complex tasks. This guide covers architecture patterns, handoff protocols, and production deployment strategies.
A single AI agent trying to do everything is like a solo developer building an entire company's software. Multi-agent systems decompose complex tasks into specialized roles. The Agent Orchestrator manages this coordination — assigning tasks, handling handoffs, and resolving conflicts. The key insight: specialist agents outperform generalists on every measurable dimension. A research agent using Deep Research produces better citations than asking a general agent to research. A coding agent with Code Reviewer catches more bugs than a general-purpose review. Combine GPT Prompt Chainer for pipeline sequencing with LLM Router for cost-efficient model selection. Use Context Window Manager to manage information flow between agents.
The most successful multi-agent deployments follow a five-agent pattern: 1. Coordinator Agent — Powered by Agent Orchestrator, it decomposes tasks and manages the team. 2. Research Agent — Uses Deep Research, Academic Search, and Web Scraper Pro to gather information. 3. Analysis Agent — Processes data with RAG Pipeline and Knowledge Graph, producing structured insights. 4. Execution Agent — Takes action using specialized skills: GitHub Manager for code, Email Automator for communication, or CRM Connector for sales. 5. Quality Agent — Validates output using Model Evaluator, Code Reviewer, or Test Generator. See our AI & LLM skills guide for the foundational skills and our coding agents guide for the developer-focused workflow.