https://video.ut0pia.org/videos/watch/778a1969-3eed-474b-a667-6a29921f3e87 The naive solution is truncation. The obvious solution is summarization. Neither worked — and the Arize team found out the hard way while building an AI agent that had to analyze the very trace data it was generating. A year of lessons from building Alyx, starting with the vicious loop that defined the problem: Alex runs on trace data, the spans grow, the context limit hits, it fails and tries again. The talk covers why truncation breaks reasoning, why summarization gives the LLM too much control, and how head/tail preservation with a retrievable memory store is what actually held. Then: long session evals, sub-agents as the answer when one context accumulates too much, and what they found when they went looking for secrets in the Claude Code source release. Speaker info: https://www.linkedin.com/in/sallyann-delucia-59a381172/, related links Mon, 11 May 2026 17:27:03 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://video.ut0pia.org https://video.ut0pia.org/lazy-static/avatars/0287a09a-aae7-4840-9843-b416426e7046.webp https://video.ut0pia.org/videos/watch/778a1969-3eed-474b-a667-6a29921f3e87 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.