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Why do AI systems silently fail? How can you set up a system that produces results while also reviewing and validating the work? This week on the show, Calvin Hendryx-Parker returns to discuss his recent talk "Orchestrate Agentic AI: Context, Checklists, and No-Miss Reviews."
👉 Links from the show:
Calvin describes how silent failures can occur when handing a large document to an LLM for parsing. He describes it as the tragedy of context, where the LLM is confident, but you have no idea what it didn't read. We discuss issues with file formats, dropped attachments, and silent truncation.
Calvin shares the example project from his talk, which includes Markdown files for the agents, hooks, skills, and scripts. He also discusses a variety of coding agents, skill frameworks, and CLI tools.
Topics:
- 00:00:00 -- Introduction
- 00:02:19 -- Co-hosting Python Bytes
- 00:03:43 -- Why AI Fails Silently talk
- 00:11:07 -- Digging into silent failures and an audit trail
- 00:18:44 -- How would you define hooks?
- 00:20:48 -- Document extraction, embeddings, and Cowork
- 00:30:38 -- Stripping the noise from file formats
- 00:34:43 -- Things that are now baked into models
- 00:45:53 -- Video Course Spotlight
- 00:47:23 -- Leveraging agents to pick models
- 00:51:44 -- GitHub repo for the project
- 00:54:57 -- The current market around tokens
- 01:00:12 -- What are you excited about in the world of Python?
- 01:03:02 -- What do you want to learn next?
- 01:04:21 -- The content the LLMs
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2026/08/21
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