Field notes on building, governing, and shipping enterprise AI — from architecture and agentic systems to operating models, governance, and what actually happens between a working demo and a production system.
Enterprise AI · Agentic Systems · Enterprise Architecture · CRM Transformation · Governance · Field Notes · Frameworks
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The Gap Between Demo and Production Is a Governance Gap
Most enterprise AI pilots fail in the same place: not on model quality, but on ownership, evaluation, and accountability. Here is the pattern, and the three things missing more often than not.
Chatbots Answer. Agents Act. Plan for the Difference.
Agentic workflows change the risk surface and the testing burden, not just the demo. A framing for teams moving from retrieval chat to systems that take action.
Retrieval Beats Model Size More Often Than Teams Expect
Notes from building a document-intelligence application where retrieval quality, not the underlying model, decided whether it was usable in production.
Who Owns the Agent When It Is Wrong?
Accountability is the question enterprise AI programs answer last and need first. A practical way to assign ownership before an agent reaches users.
An Operating Model for Human-in-the-Loop AI
Where review belongs, what to automate, and how to keep people meaningfully in control as agentic systems scale across an enterprise function.