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
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.
Agent drift: why long-running AI agents lose the plot
Agent drift is how AI agents silently deviate from goals over long-running tasks. Six mechanisms cause it, and most have nothing to do with the model.
Context Poisoning: When Bad Data Becomes AI Ground Truth
Context poisoning plants false data into an AI agent's memory or RAG index. The model treats it as truth. It's a context engineering problem, not a model bug.
Why multi-agent AI systems fail at context
Up to 86.7% of multi-agent AI runs fail. Most failures trace back to how agents share context, not the agents themselves. Here's why and how to fix it.
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