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If coding agents handle more implementation, the human role does not disappear. It moves toward mission, judgment, validation, integration, accountability, and helping more organizations use software well.
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Mission-Driven Engineering did not start as a framework. It started when I realized I was using coding agents to manage UI screens, data layers, and implementation artifacts instead of asking them to satisfy the outcome I actually cared about.
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A practical walk-through of Mission-Driven Engineering: how missions, validations, generations, learning loops, and shared MDE memory turn AI coding agents into application-generation systems.
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AI application generation feels new, but it echoes Model-Driven Engineering: humans describe intent, machines generate implementation, and independent validation decides whether the result actually works.
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AI coding agents can make implementation dramatically faster, but they also create a new bottleneck: the human cost of managing context, attention, and learning across many parallel projects.
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A website is only the front door. In this article, I explore how an AI-enabled conversation becomes part of a small operations system for leads, clients, jobs, reviews, videos, and ads.
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As AI coding agents improved, the generated code became less interesting than the final application outcome. The question shifted from whether the code looked right to whether the application solved the problem.
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Most of my interaction with coding agents became copying error messages from build systems and asking the agent to fix them. That raised an uncomfortable question: why was I in the loop at all?
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I initially trusted AI through code generation because code could be validated. What surprised me was discovering that the hardest problem was no longer implementation, but defining when the work was actually complete.
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Building the website was easy. The more interesting question was whether a small business could afford an AI-powered customer experience with virtually no recurring software costs.
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Agile transformed software development by adapting to changing requirements. But what happens when AI dramatically reduces the cost of implementation? Are we still optimizing for the right bottleneck?
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What if the biggest obstacle to AI transformation isn’t the technology, but the way we’ve organized our companies?
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In my previous article, I argued that AI-assisted development may be bringing an end to software scarcity. This article tests that idea through a real-world project with a local small business owner.
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For decades, organizations adapted themselves to software because software was expensive to build and maintain. AI-assisted development may reverse that relationship, making it practical for software to adapt to the unique needs of individual organizations.
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In this article, I explore the evolution of retail search and attempt to predict the future of retail search
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Moving beyond simple keyword matching. How the industry transitioned to hybrid systems, query understanding, and the “Builder’s Era” of search relevance.
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A look back at the “Black Box” era of retail search, why we moved away from it, and the fundamental tension between finding products and making money.
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Between 2017 and 2025, I consistently taught undergraduate or graduate courses in most semesters while balancing my teaching responsibilities with a full-time position in industry.
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Teaching methods should be flexible, evolving based on the students, course content, and learning environment.