Anthropic just released Opus 4.6, introducing a breakthrough 'agent teams' capability that lets multiple AI agents coordinate on complex tasks simultaneously. The upgrade, arriving just three months after Opus 4.5's November debut, marks a strategic pivot for the company - transforming what was primarily a developer tool into a broader enterprise productivity platform. With a million-token context window and native PowerPoint integration, Anthropic is making a serious play for knowledge workers beyond the engineering department.
Anthropic is rewriting the rules of AI collaboration. The company dropped Opus 4.6 on Thursday, and the headline feature - agent teams - represents a fundamental shift in how AI assistants tackle complex work.
Instead of one AI agent plowing through tasks sequentially, Opus 4.6 lets you deploy multiple agents that divvy up the work and coordinate in real-time. "Instead of one agent working through tasks sequentially, you can split the work across multiple agents - each owning its piece and coordinating directly with the others," according to Anthropic's announcement. Scott White, Head of Product at Anthropic, told TechCrunch it's like having a talented human team working for you, with agents able "to coordinate in parallel [and work] faster." The agent teams feature is rolling out now in research preview for API users and subscribers.
This isn't just a technical flex - it's Anthropic acknowledging that its users have evolved way beyond the developer crowd. White revealed that the company noticed something unexpected: people who aren't professional software developers were flocking to Claude Code "simply because it was a really amazing engine to do tasks." Product managers, financial analysts, and workers from all kinds of industries started using what was essentially a coding tool because it just worked better than alternatives.
So Anthropic leaned into it. Opus 4.6 expands its context window to 1 million tokens - putting it on par with what the company's . That's enough headroom to handle sprawling codebases or process hefty documents without losing the thread. For enterprise teams juggling complex projects with mountains of documentation, that context capacity isn't just convenient - it's essential.
