The hard part of AI products is rarely the first demo. The hard part is making the system survive real data, real latency, real permissions, real edge cases, and real user expectations.
My recent work across Cavelight, medical agents, crypto analysis, and developer tools has kept pointing to the same lesson: the product value is in the workflow around the model.
What matters after the demo
- Context has to be built, audited, cached, and refreshed.
- Retrieval quality has to be measured, not guessed.
- Agents need bounded tools, fallback behavior, and observability.
- Data pipelines and permission models matter as much as prompt quality.
- Deployment and latency shape what the user can actually rely on.
This site is where I will keep notes on those details.