AI agents can write code and answer questions, but they still fall apart on long, messy jobs. A Mountain View startup just raised $40M to build the training grounds that fix that.
Bespoke Labs, which builds the environments that train and test AI agents, has raised $40 million, the company announced. The total spans a Series A led by Wing VC and an earlier seed led by 8VC. The backer list is unusually pointed: angels who work at Anthropic, OpenAI, and Meta, plus Google DeepMind’s Jeff Dean and dbt Labs chief Tristan Handy.
Practice grounds for agents
Today’s agents are capable but unreliable. They handle short tasks well. They still struggle to work on their own over hours or days, the way a colleague would. Bespoke’s bet is that the fix is not a bigger model but a better place to practise.
So it builds simulated versions of real firms: large codebases, microservices, logs, support tickets, email, and Slack threads. Agents train inside these worlds and learn the long, multi-step workflows that actually earn their keep. Bespoke then helps customers measure and tune them, using an in-house optimiser it calls GEPA to find better prompts and policies faster than hand-tuning allows.
