Closing a chapter at PwC
A reflection on building and deploying AI in a high-stakes product environment before beginning a new venture.
Read article →Writing archive
Eleven notes on agent architecture, governance, reliability, model economics, and professional development.
A reflection on building and deploying AI in a high-stakes product environment before beginning a new venture.
Read article →Anthropic’s advanced certification for architects designing and governing Claude solutions at enterprise scale.
Read article →As agent systems become active web users, products need explicit identity, permissions, interfaces, and evidence trails.
Read article →A six-week partner cohort, hands-on labs, technical reviews, and practical infrastructure work strengthened my multi-cloud foundation.
Read article →AI lowers the barrier to persistent, adaptive automation—and raises the reliability demands placed on the systems underneath it.
Read article →I earned GitHub's Agentic AI Developer credential after taking the exam during its beta period.
Read article →A multi-agent system creates risks through the sequence of actions its participants can take together.
Read article →One real coding task cost roughly 80 times more with one frontier model than another, with negligible difference in the final result.
Read article →A system should grant agents more room to act only when risk, evidence, and reversibility support it.
Read article →Event-driven architecture gives AI agents a cleaner way to coordinate, fail, recover, and evolve inside real enterprise platforms.
Read article →Cursor, Claude, Copilot, and custom agents are most valuable when they become part of a governed engineering workflow instead of a side tool.
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