The Algorithm
Notes on building AI systems that actually work.
What I've learned building AI systems across healthcare, finance, ecommerce, and government — the architecture decisions, design tradeoffs, and organizational dynamics that determine whether an AI project succeeds or stalls.
Latest
I Stopped Prompting AI. I Started Assigning Work.
The problem with prompting isn't that you're doing it wrong. It's that prompting puts you in the wrong role. When you're the context-carrier every session — restating standards, reloading domain knowledge, correcting the output — you're not delegating. You're operating a tool with no institutional memory.
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Who Authorized That Decision?
Organizations point their AI at the policy document and assume that counts as enforcement. It doesn't. The gap between pointing at rules and delegating authority to enforce them is where hidden governance exposure lives.
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What Actually Makes AI Work in Production
A model can interpret a request, draft a response, and still fail in production. Reliable AI systems need interpretation, boundaries, context, measurement, and human judgment working together.
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Series
Foundations
The analytical and structural thinking that everything else builds on.
8 postsStop Thinking in Tools
Why the tool is never the point, and what to focus on instead.
4 postsCompound Agent OS
How an AI operating system of specialized agents is designed, structured, and improved over time.
2 postsSubscribe to The Algorithm
Notes on building AI systems that actually work.