Anthropic工程师Ash Prabaker与AndrewWilson分享打造长时程可靠AIAgent的实战经验。核心看点：1. 为何自评估是陷阱，对抗性评估器更有效；2. 结构化交接优于上下文压缩，解决连贯性漂移；3.用可测试的sprint合约分解复杂任务；4. 设计LLM可执行的评分标准评估主观输出；5.通过trace分析定位Agent问题。适合开发者构建生产级多轮任务系统参考。
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原视频标题：Build Agents That Run for Hours (Without Losing the Plot) — Ash Prabaker & Andrew Wilson, Anthropic
原视频作者：AI Engineer
原视频链接：https://www.youtube.com/watch?v=mR-WAvEPRwE
原视频发布时间：2026-05-18 21:00:06
原视频简介：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/
Timestamps:
0:00 Introduction and speakers
1:21 Overview of long-running agents
2:29 Challenges: Context, Planning, and Judgment
4:14 Two approaches: Model updates vs. Harness evolution
5:58 Prehistory: Sonnet 3.5, Computer Use, and MCP
6:34 The evolution of Claude Code
7:55 The Ralph loop technique
9:49 Sonnet 4.5, Agent SDK, and checkpoints
10:49 Opus 4.5 and the role of sub-agents
12:05 First long-running agent patterns
14:20 Opus 4.6, Agent Teams, and server-side compaction
17:28 State-of-the-art harness patterns
21:30 Evaluating subjective output with rubrics
23:44 Introducing the 'Planner' role
25:04 The generator-evaluator contract
31:28 Specificity in contracts and debugging traces
34:14 Adjusting harnesses as models evolve
37:56 How to build your own agent harness
39:01 Key takeaways for long-running agents
40:05 Q&A session
