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Anthropic tells companies one AI agent often beats many

Its Applied AI team says a single agent with skills often did better on quality, cost and speed than a coordinator running a team of specialist agents.

By Alexandre S. , 00:30 UTC

Firms that build AI assistants for their customers often split the job across many smaller agents. Anthropic now argues that a lot of them should merge back into one, in advice The Decoder covered on 25 October 2026. Anthropic’s Applied AI team compared setups at several large companies, and the simplest often came out ahead: one agent with skills, sets of instructions it opens only when a task calls for them. That design often gave better answers than a team of subagents or one catch-all prompt, and it was often cheaper and quicker, Anthropic writes.

Retro-futurist illustration: a striped sunset over a grid horizon under a starry sky, with a microchip standing on the horizon.
Drawn by adtestbench from “Building agents: when to fold subagents into your main agent loop”,

Think of a subagent as a helper that a lead agent hands work to, each with its own toolset, instructions and working memory. One company’s customer-facing agent leaned on a dozen specialists, and Anthropic says the result answered slowly and struggled to sound like the same brand from reply to reply. The cause, Anthropic says, is often the org chart: every team ships its own agent, and their rules start to clash in the system prompt. Subagents still make sense for parallel tasks or when one agent’s context grows too large.

In one comparison the single agent ran about twice as fast, and Anthropic has released a migration kit on GitHub, The Decoder reports.

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On Anthropic’s account, a shop assistant meant to speak in one voice is easier to keep on-brand as one agent with one centrally owned system prompt than as a dozen agents written by different teams.