This is the actual checklist I work from when taking an AI product from idea to scale: cross-cutting themes, then strategy, engineering, orchestration, and growth. It is long on purpose. You are meant to cherry-pick what fits your situation, not follow it like a recipe.
We are moving beyond text generation to autonomous execution. Defensibility no longer comes from the model itself (which is commoditized), but from the orchestration of agentic workflows that integrate with proprietary data.
Focus on building systems that solve complex, multi-step business problems through reliable tool-calling and recursive state management.
Most Gen AI experiments never make it to production, and it is usually an engineering problem rather than a model one. This playbook focuses on the work that makes agents reliable, observable, and safe enough for real enterprise use.
Build products that fundamentally change how work gets done, creating new value chains through agentic autonomy.
Always start with customer needs, validate continuously, and iterate based on feedback. In AI products, this means understanding not just what customers want, but how AI can fundamentally change their workflows and outcomes.
Define metrics early, measure everything, let data guide your strategy. AI products require sophisticated measurement frameworks that capture both technical performance and business impact.
Ship fast, learn quickly, and iterate. Perfect is the enemy of good. In AI development, rapid iteration is crucial for model improvement and feature validation.
Break down silos, align teams, and work together toward shared goals. AI products require deep collaboration between product, engineering, data science, and business teams.
I refine this as the agentic stack changes. Drop your email and I will send the occasional update, plus new deep-dive posts. No spam.
If you want a second set of eyes on strategy, architecture, or what to ship next, let us talk through it.