Notes and essays on AI agents, AI-augmented development, software delivery, architecture, and the practical lessons behind digital platforms.
The bottleneck in long-running agent coding isn't the model or the prompts. It's the state management layer. Linear, OpenAI, and Anthropic's rumored Atlassian move all point to the same conclusion.
How AI-augmented development is shifting what developers actually do, and why the next level isn't about writing code anymore.
Part 5 of a series on enterprise AI governance. Skills as the control plane, phased rollout, audit cadences, incident response, and what the framework adds up to.
Part 4 of a series on enterprise AI governance. Identity, three-layer observability, user grouping, and policy enforcement that produces a defensible audit trail.
Part 3 of a series on enterprise AI governance. Threat modeling using OWASP LLM Top 10, OWASP Agentic Top 10, MITRE ATLAS, and MCP-specific defense patterns to model real risk.
Part 2 of a series on enterprise AI governance. The standards you build on (NIST AI RMF, ISO 42001), the gaps they leave on agents and MCP, and the two-layer operational model that closes them.
Why the future of engineering leadership depends on intent, context, and collaboration between humans and intelligent systems.
How engineering organizations operationalize AI-augmented development, build the tooling and processes, and deliver at scale.
A data-backed framework for scaling AI’s impact across people, platforms, and process.
Why AI coding agents mark a deeper shift in how we design, deliver, and define software itself.
Occasional notes on AI agents, software delivery, and AI-assisted engineering.
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