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title: How and when to build multi-agent systems | daily.dev
description: Harrison Chase from LangChain synthesizes insights from two recent blog posts — Cognition's 'Don't Build Multi-Agents' and Anthropic's 'How we built our...
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# How and when to build multi-agent systems

**[LangChain](https://daily.dev/sources/langchain)** · 8 min read · 0 upvotes · 0 comments

## Summary

Harrison Chase from LangChain synthesizes insights from two recent blog posts — Cognition's 'Don't Build Multi-Agents' and Anthropic's 'How we built our multi-agent research system' — to distill when and how to build multi-agent AI systems. Two core principles emerge: context engineering is the #1 challenge for agent builders, and multi-agent systems designed for 'reading' (parallelizable research tasks) are far more tractable than those involving 'writing' (e.g., code generation). Production reliability challenges like durable execution, observability, debugging, and evaluation are also covered, with LangGraph and LangSmith positioned as solutions to these generic infrastructure problems. Multi-agent architectures work best for breadth-first, high-value tasks that exceed single context windows, while tasks with many inter-agent dependencies (like most coding) are poor fits today.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.langchain.com/blog/how-and-when-to-build-multi-agent-systems>

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#context-engineering](https://daily.dev/tags/context-engineering), [#langgraph](https://daily.dev/tags/langgraph), [#langsmith](https://daily.dev/tags/langsmith)

[View this post on daily.dev](https://daily.dev/posts/how-and-when-to-build-multi-agent-systems-ux6mep5gr)

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