Definition
What is a Multi-Agent System?
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An architecture where multiple AI agents collaborate on a task, each with its own context window, tools, and responsibilities.
Multi-agent systems divide complex work across specialized agents (e.g., a planner, a researcher, a coder) that coordinate through structured handoffs. The main challenge is context management: how agents share information without leaking irrelevant state, duplicating tokens, or operating on stale data. Effective multi-agent architectures scope context per agent and summarize at handoff boundaries.
Further reading
Articles about Multi-Agent System
Five criteria of good context for AI agents
A 2026 paper formalizes five criteria for good AI agent context: relevance, sufficiency, isolation, economy, and provenance. Here's how to design for each.
Sub-agent context isolation: the fix for context rot
Sub-agent context isolation gives each agent its own scoped window, stopping the context rot that kills multi-agent runs. Here's the pattern and its limits.
Context offloading: 3 patterns for AI agents
Context offloading keeps an AI agent's working context window small by moving state to a destination outside it. Three patterns, and what each one costs.
Why every agent handoff corrupts your context
Every multi-agent handoff is a lossy compression event. Learn which five types of context degrade at agent handoff boundaries and how to preserve them.
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