Don't get tempted to build multi-agents

Linear, continuous LLM flows are all you need

These are my notes from https://cognition.ai/blog/dont-build-multi-agents

• Context is EVERYTHING. Entire context is full of implicit decisions an LLM is making, and if you don’t share full context across the task, the chances of different submodules/agents diverging increases significantly

  • Think of it this way. If you can get away with doing some work yourself, you’d do that as communication has overheads and having a larger team in human-world only makes sense because humans get tired (but LLMs don’t)

  • So, do not import concepts from human world into LLM world

• Also, an LLMs latent state communicates way more than just sampled tokens (so when you just share sampled tokens with another module/agent, you risk losing significant amount of information)

• So, if you can get away with building a single, linear system using LLMs, always prefer that over a mix of agents

  • For complex tasks, you will run into context length not being enough, but solve that as a separate issue (e.g. use a separate compression module for passing context)

• Multiple agents make sense a) when you need speed, AND b) when tasks are de-coupled and divergence doesn’t matter

  • E.g. explore things in parallel (but even there, a single agent exploring sequentially is likely to be more diverse than parallel exploration, unless parallel explorations are prompted very differently)

• Most current systems are single agent sharing entire context

  • Gemini CLI, Claude Code

  • Even editing models in IDEs like cursor are the same as chatting models as shared, continuous context helps in precise editing

TLDR, give me a meme:

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