An LLM feature has four parts that change on their own. Release them as one versioned artifact behind an eval gate. The rules, tables and build order.
Why LLMs hallucinate, the three kinds of AI hallucination, and which controls actually reduce it, ranked by cost and matched to the cost of an error.
Deflection counts every chat that never reached a human, including the customer who gave up. What to measure instead, and what to ask vendors.
Most systems called agents should be workflows with a model call inside. The four shapes, a scoring rubric, and when autonomy is worth what it costs.
LLM document extraction reads layouts OCR templates cannot, and invents fields it cannot read. When it beats IDP, what it costs, and the controls it needs.
The AI platform mandate lands on the team that owns the gates. It is the second platform consolidation, and the scope has to be renegotiated on the way in.
When an agent moves money or promises a customer something, the loss lands wherever the contract is silent. That gap is a deployment constraint.
When an agent fails in production the cause is usually a badly designed tool, not a weak model. The granularity, description, parameter and return rules.
Prompts, completions, embeddings, and logs land in four different places. What a regional endpoint guarantees, what it only implies, and how to decide.
The best vertical AI companies stopped selling assistants beside the suite. They are taking over the record itself, and the record owns the renewal.
Agent memory is a retention policy, not a database. The four memory classes, write and read rules, forgetting as a feature, and the store-or-recompute math.
Container, microVM, or separate account: what each agent sandbox rung actually stops, why egress policy matters most, and how to scope agent credentials.
Deep analysis across the systems, strategies, and economics that shape modern technology.
Premium Members Get: Exclusive deep-dive research · Architecture playbooks · Executive briefings · Full archive access
