7 posts on this topic
The right model for enterprise AI is not 'better search.' It is compressed onboarding. A real enterprise second brain teaches an agent what a strong new hire learns in the first six months, then turns that knowledge into reliable action.
If agents are blank slate coworkers, product strategy can no longer live in Slack, Notion, or someone else's head. It has to become part of the execution environment.
RLHF-trained coding agents do not just make mistakes. They silently implement the wrong thing, accruing alignment debt that passes tests and leaves the codebase worse off.
If your workflow has no unknowns, runtime LLM inference is often the wrong architecture. Build deterministic cores, then add AI where adaptation actually matters.
The AI leaderboard tells you which model reasons best in isolation. It tells you almost nothing about which model completes real work.
Agentic harnesses solve the orchestration problem. The models are the bottleneck. Here's what actually works after 43 PRs and a zettelkasten full of operational data.
The most interesting thing about the purveyors data pipeline isn't the scraping. It's the recursive feedback loop, and what it reveals about directing AI agents.