AI Operator track — production AI agent engineering. Validation loops, tool-call reliability, completion ownership, context engineering, cost forensics, and observability for systems that ship and stay shipped.
Long-form writing and video on production AI agent engineering — the 90% of work that happens after the model gives you a demo that works.
Topics: validation loops, tool-call completion ownership, context engineering, retry budgets, cost forensics, and observability for systems that ship and stay shipped.
Companion to the Harrison AI Operator YouTube channel. Blog and video are listed below — most recent first.
The MEMORY.md frontmatter spec from Claude Code's leaked source: 4 types (user, feedback, project, reference), 200-line index cap, LLM-based picker. What's right, where it breaks at scale.
Claude Code v2.1.88 accidentally exposed 510K lines. The 5 hidden features: Kairos (permanent memory), Undercover Mode (stealth), Ultraplan (deep planning), Pet System (Buddy), Multi-Agent. Source-cited.
Extended 48-minute version of Episode 1. Same LLM-talks-program-walks framework, but with deeper exploration of every layer from token streaming to multi-agent orchestration.
Extended podcast version of the AI Stack thesis. LLM, Token, Context, Function Calling, MCP, Agent, Skill — eight concepts that confuse every engineer until you see they're all the same pattern.
A first-principles breakdown of the entire AI stack — from LLM to Agent in one mental model. An LLM can only output text. Everything else is the program.
A first-principles breakdown of the entire AI stack in 15 minutes — from LLM to Agent. One mental model: the LLM can only output text, the program does everything else.
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