{"version":"1.0","type":"card","id":"b37ae430-26cd-4744-ab24-a3e196e27d30","url":"https://stacklist.com/card/b37ae430-26cd-4744-ab24-a3e196e27d30","title":"Bespoke Labs raises $40M for AI agent training environments","source_url":"https://thenextweb.com/news/bespoke-labs-40m-ai-agent-training-environments","note":"Bespoke Labs has raised $40M from Wing VC, 8VC and Anthropic, OpenAI and Meta insiders to build the environments that train and test AI agents. This funding aims to enhance the reliability and effectiveness of AI systems through specialized training grounds.","image":{"url":"https://ucarecdn.com/b44e9f86-8142-455d-9bff-03e1c4c5b97c/","alt":"Bespoke Labs raises $40M for AI agent training environments","width":868,"height":488},"stack":{"id":"b4b2a9ee-24c0-4e11-92f5-3eca6f0ce7a2","title":"Signals Served #019 - Regulation Became the Moat","url":"https://stacklist.com/c/finance/stack/b4b2a9ee-24c0-4e11-92f5-3eca6f0ce7a2"},"created_at":"2026-07-21T20:15:48.445Z","updated_at":null,"aco":{"summary":"Bespoke Labs has raised $40 million across a seed and Series A round to build simulated enterprise environments that train and test AI agents on long, multi-step workflows. The Mountain View startup, founded in 2024, bets that better practice grounds, not bigger models, are the key to making agents reliable enough for production use.","tags":["ai-agents","training-environments","funding","bespoke-labs","series-a","agent-reliability","simulation"],"key_entities":[{"name":"Bespoke Labs","type":"organization","confidence":0.99},{"name":"Mahesh Sathiamoorthy","type":"person","confidence":0.95},{"name":"Alex Dimakis","type":"person","confidence":0.95},{"name":"Jeff Dean","type":"person","confidence":0.92},{"name":"Tristan Handy","type":"person","confidence":0.88},{"name":"Wing VC","type":"organization","confidence":0.95},{"name":"8VC","type":"organization","confidence":0.93},{"name":"Anthropic","type":"organization","confidence":0.85},{"name":"OpenAI","type":"organization","confidence":0.85},{"name":"Meta","type":"organization","confidence":0.85},{"name":"Google DeepMind","type":"organization","confidence":0.88},{"name":"GEPA","type":"technology","confidence":0.9},{"name":"Terminal-Bench","type":"technology","confidence":0.9},{"name":"OpenThoughts","type":"technology","confidence":0.9},{"name":"METR","type":"organization","confidence":0.85},{"name":"Mountain View","type":"location","confidence":0.95},{"name":"agent training environments","type":"concept","confidence":0.95},{"name":"dbt Labs","type":"organization","confidence":0.85}],"classification":"analysis","language":"en","confidence":0.85,"provenance":{"model":"claude-opus-4-6","tool":"@stacklist/mcp-server@2.0.0","confidence":0.85,"timestamp":"2026-07-21T20:15:58.302Z"},"token_counts":{"approximate":659,"cl100k":533},"content_hash":"sha256:ae325eb0f06f7e66c600796fb677a72aba451cf83db9017bac15d6156ef6f0b6","acp_version":"0.2","body_available":true,"body_tokens":659,"visibility":"public","agent_accessible":true,"status":"final"},"_links":{"self":"/api/public/card/b37ae430-26cd-4744-ab24-a3e196e27d30.json","html":"https://stacklist.com/card/b37ae430-26cd-4744-ab24-a3e196e27d30","md":"/api/public/card/b37ae430-26cd-4744-ab24-a3e196e27d30.md","stack_json":"/api/public/stack/b4b2a9ee-24c0-4e11-92f5-3eca6f0ce7a2.json"}}