[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-a-dataset-of-50000-ai-agent-failures-diagnosed":10,"sections":35},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},8970,"a-dataset-of-50000-ai-agent-failures-diagnosed","A Dataset of 50,000 AI Agent Failures, Diagnosed","A new 50,000-pair dataset teaches AI agents to diagnose and fix their own failures, with early fine-tuning tests showing real gains over prompting alone.","A new dataset turns 50,000 AI agent failures into lessons models can actually learn from.\n\nThe Agent Error Dataset (AED) contains 50,228 error diagnosis pairs pulled from 9,961 tasks across 33 environments, 19 agent harnesses, and 23 policy models. A five-stage pipeline called Agentic Error-to-Training collects real failures, proposes diagnoses and fixes, then checks those fixes against recorded evidence instead of guesswork. In 3,062 replay tests where researchers reran the original failed step, the first proposed correction raised verifier pass rates from 18.4% to 51.1%. Fine-tuning Qwen3-8B on the diagnosis data raised its agreement with internal teacher labels from 47.2% to 63.6%, beating the best prompted baseline's 54.7%.\n\nMost agent benchmarks score a run pass or fail and throw away everything else. This approach treats the failed attempt itself as useful data, and a separate test found that training on failure repairs alone beat training on success alone by nearly 7 points on WebShop-lite. That is a real signal that teaching an agent to diagnose what went wrong may matter more than just rewarding it when things go right.\n\nIt is still a research-lab result built on a few thousand matched replay pairs, not a production agent that debugs itself in the wild, so treat the percentages as promising, not proven.","[\"ai-agents\",\"llm-training\",\"benchmarks\",\"research\"]","2026-10-01T04:00:00.000Z","2026-10-01T13:23:36.512Z","2026-10-01T13:23:42.748Z","published",null,[],"ai",[26,27,28,29],"ai-agents","llm-training","benchmarks","research",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.40111",0,{"sections":36},[37,40,44,49,54,59,63,68,73,77,82,87,92,97],{"name":38,"slug":24,"count":39,"latest_published_at":18},"AI",5455,{"name":41,"slug":42,"count":43,"latest_published_at":18},"Security","security",805,{"name":45,"slug":46,"count":47,"latest_published_at":48},"Policy","policy",429,"2026-10-01T02:26:17.000Z",{"name":50,"slug":51,"count":52,"latest_published_at":53},"Deals","deals",298,"2026-09-30T21:00:26.000Z",{"name":55,"slug":56,"count":57,"latest_published_at":58},"Hardware","hardware",196,"2026-09-30T13:00:00.000Z",{"name":60,"slug":61,"count":62,"latest_published_at":18},"Science","science",159,{"name":64,"slug":65,"count":66,"latest_published_at":67},"Consumer Tech","consumer-tech",149,"2026-09-30T22:57:11.000Z",{"name":69,"slug":70,"count":71,"latest_published_at":72},"Dev Tools","dev-tools",93,"2026-10-01T02:30:48.000Z",{"name":74,"slug":75,"count":71,"latest_published_at":76},"Software","software","2026-09-30T21:41:11.000Z",{"name":78,"slug":79,"count":80,"latest_published_at":81},"Startups","startups",84,"2026-09-30T20:39:09.000Z",{"name":83,"slug":84,"count":85,"latest_published_at":86},"Gaming","gaming",51,"2026-09-30T16:24:30.000Z",{"name":88,"slug":89,"count":90,"latest_published_at":91},"General","general",50,"2026-09-30T21:37:54.000Z",{"name":93,"slug":94,"count":95,"latest_published_at":96},"Reviews","reviews",31,"2026-09-28T14:31:34.000Z",{"name":98,"slug":99,"count":100,"latest_published_at":101},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]