Anthropic says one AI agent beats many subagents now that models can handle complexity alone

Anthropic found that a single agent with on-demand context often outperforms multi-agent setups in quality, cost, and latency. The company is releasing a migration kit to help teams evaluate and make the switch.

Anthropic is recommending that companies evaluate whether they can consolidate sub-agents, because a single primary agent that loads context as needed often delivers better results than a fleet of specialized ones. Newer models starting with Opus 4.5, along with Opus 5.5 and Fable 5.1, can manage complex plans directly in a single loop, which can also reduce costs and wait times. In one comparison, Anthropic says the single-agent architecture ran about twice as fast as the multi-agent setup.

The recommendation comes from hands-on experience across multiple enterprise deployments, where Anthropic's Applied AI team found that a single agent using so-called skills frequently beat both monolithic prompt designs and sub-agent setups on quality. One company, for example, had a dozen specialized sub-agents inside its customer-facing agent, which made it hard to keep a consistent brand experience and hurt usability with high latency.

The model can now do much of the orchestration work itself

According to Anthropic, many agent designs follow a familiar pattern with intent detection, a fixed processing pipeline, user segmentation, separate recommendation branches, and an orchestrator sitting on top. Much of that is work the model can now handle on its own for the respective use case.

Anthropic calls it "Conway's Law" for agents. The architecture becomes a copy of the org chart, with each team shipping its own sub-agent until hundreds of overlapping agents pile up with no shared standards. What follows is what Anthropic calls a "Frankenprompt," where every team adds rules to the system prompt that end up contradicting each other. A clean system prompt, four or five lean data tools, and fixed output guardrails cover much of the respective use case, according to the company.