I published a monthly‑focused guide for product managers on how to adopt and operationalize new AI tools, built around a durable six‑step hierarchy that stays constant despite tool churn.
I define the five components of any real agent as model, knowledge, memory, tools, and guardrails – a rule I attribute to Mahesh Yadav.
I note that AI gross margins run 20‑60 % versus 70‑90 % for traditional SaaS.
I hand‑label about 100 real interaction traces to evaluate LLM judges, then compare raw agreement to true‑failure detection rate.
My content‑business workflow uses 51 distinct skills, organized by area‑folder and task‑skill.
I recommend a context window of one‑to‑two weeks of data, adding retrieval only for facts outside the model’s training.
I prioritize cost‑first optimization in the order: model routing → caching → pricing.
These decisions reflect my belief that a fixed hierarchy protects product teams from endless tool churn while keeping the focus on economic viability.