Notes on the agentic enterprise.
What changes when AI stops talking and starts acting. Field notes on governance, audit, and the quiet infrastructure layer that the next decade of software will be built on.
Latest
Policy Rules Should Be Tested Before They Fire
Every other category of software configuration is tested before it reaches production. Policy rules that govern AI agents are not. That gap is going to matter.
May 27, 2026
The Automation Reflex Breaks Down at the Governance Layer
The instinct to automate everything that is slow or expensive is almost always right. Governance is the exception - and understanding why matters for any team deploying agents at scale.
May 20, 2026
There Are Two AIs in Every Agentic System. Most Teams Only See One.
AI agents are reshaping enterprise software. So is AI tooling for the people who run enterprise software. Conflating these two is one of the most common architectural mistakes we see.
May 1, 2026
The Quiet Infrastructure Layer of the Next Decade
The categories that end up running the world rarely arrive with a flag and a parade. They arrive as the answer to a problem that everyone has and nobody has named yet.
Apr 25, 2026
Agents Should Speak Intent, Not API
Every team building agents eventually rediscovers the same lesson: making the agent learn each provider's API turns out to be the wrong abstraction. The right one looks more like a verb.
Apr 19, 2026
What an Audit Trail Actually Means in the Age of AI
When the actor is a probabilistic system, an audit trail is no longer a forensic tool. It becomes the only honest answer to 'what did our software actually do?'
Apr 10, 2026
The Three Things That Break When Agents Touch Production
Most agentic projects don't fail at the model. They fail at the joints - the places where intent meets identity, policy, and consequence.
Apr 2, 2026
From Conversation to Consequence
The most important shift in software in a decade is not that AI got smart. It is that AI started taking actions whose effects outlive the chat.
Mar 24, 2026
The Boundary Problem
Models are getting better fast. The thing that determines whether AI is useful at work is no longer the model - it's the boundary the model crosses to do anything that matters.