Feb 9, 2026 - 7 minute read

Every Agent Memory Architecture Fails Differently

I’ve been running multi-agent teams for months now. Agents writing code, generating images, managing deployments, drafting content, reviewing each other’s work. It’s the most productive workflow I’ve found — and the most fragile. Not because the models are bad or the tools are missing. The thing that keeps breaking is memory. How do agents remember what matters across conversations, across tools, and across each other? I’ve tried four different approaches. Each one solved one problem and created two more.

Feb 8, 2026 - 4 minute read

Useful, Autonomous, Safe — Pick Two

Every team building AI agents hits the same wall. You want the agent to be useful — capable of real work with real tools. You want it autonomous — running without a human babysitting every action. And you want it safe — resistant to manipulation, predictable in behavior. The AI Assistant Trilemma says you only get two. Until prompt injection is fundamentally solved, this constraint is as hard as the CAP theorem — and just as non-negotiable.

Feb 6, 2026 - 4 minute read

Engineering Has Three Jobs

Everyone’s rebranding engineers. “AI Engineers.” “Prompt Engineers.” “Agentic Engineers.” New titles every quarter, each one implying the old work is dead. It’s noise. The job of engineering hasn’t changed—only the tools have. Engineering exists to do three things, and it has existed to do these same three things since the first human sharpened a stick into a spear.

Feb 5, 2026 - 4 minute read

Ask Why AI Can't Do It

Most people trying to become AI-native are doing it backwards. They study AI tools. They watch tutorials. They read about prompting techniques. Then they go back to doing their work the same way they always have—manually. The shift to AI-native isn’t about learning AI. It’s about unlearning yourself as the default operator. Every time you start a task, ask one question first: “Why can’t AI do this?” The answer reveals exactly what you need to fix.