Writing
AI-for-Work: A Systems Guide to Governed AI Deployment
Enterprise AI adoption is not mainly a model-selection problem. It is an operating-system problem: tools, gateways, observability, orchestration, knowledge preservation, and adoption loops that let thousands of people work differently without losing control.
The through-line is deployment control. Strong models are becoming available to everyone. The durable advantage is the system that decides which agents can access which context, call which tools, produce which artifacts, and affect which production surfaces.
Posts in this series
Part 1: Get Everyone an AI IDE
Start with the daily developer surface before reaching for elaborate agent platforms.
Part 2: Build a Centralized MCP Gateway
Connect AI tools to internal context through one audited and permissioned layer.
Part 3: From Proxy Logs to Intelligence
Use request streams to understand adoption, risk, productivity, and cost.
Part 4: Agent Orchestration at Scale
Turn agents from demos into a managed runtime with state, permissions, and recovery.
Part 5: Preserve Institutional Knowledge
Keep the reasoning and decisions that future humans and agents will need.
Part 6: Work-as-Code
Expose repeated engineering tasks as small callable interfaces.
Part 7: Observability and Adoption Programs
Turn scattered AI usage into measurable organizational learning.
How to read it
The series is a practical map for making AI useful inside institutions that have real security, privacy, reliability, and adoption constraints. Read it as a stack: IDE surface, MCP gateway, observability, orchestration, knowledge memory, workflow APIs, and adoption loops.