Build Agents That Run for Hours (Without Losing the Plot) — Ash Prabaker & Andrew Wilson, Anthropic https://video.ut0pia.org/videos/watch/e0a1d031-97ac-4125-8e49-ae5f45a738e0 Why self-evaluation is a trap and adversarial evaluator agents work better; why context compaction doesn't cure coherence drift but structured handoffs do; how to decompose work into testable sprint contracts; how to grade subjective output with rubrics an LLM can actually apply; and how to read traces as your primary debugging loop. Plus the question nobody asks: which parts of your harness should you delete when the next model drops? Speaker info: Ash Prabaker | https://www.linkedin.com/in/ash-prabaker/, Andrew Wilson | https://www.linkedin.com/in/anddwilson/ Wed, 20 May 2026 23:07:16 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://video.ut0pia.org Build Agents That Run for Hours (Without Losing the Plot) — Ash Prabaker & Andrew Wilson, Anthropic https://video.ut0pia.org/lazy-static/avatars/0287a09a-aae7-4840-9843-b416426e7046.webp https://video.ut0pia.org/videos/watch/e0a1d031-97ac-4125-8e49-ae5f45a738e0 All rights reserved, unless otherwise specified in the terms specified at https://video.ut0pia.org/about and potential licenses granted by each content's rightholder.