AI has already changed the economics of software engineering. Code generation is becoming cheap, fast, and abundant. That moves engineering value upstream into judgment.

Agentic coding gives engineers extraordinary leverage, but it also compresses the timeline for technical debt. Agents can generate working code faster than teams can absorb it, review it, understand it, and fit it into the architecture. When engineering discipline doesn’t scale with output, teams get architectural drift, cognitive debt, duplicate logic, shallow documentation, fragile abstractions, and code nobody truly owns. 

This talk shares the engineering standards I’ve put in place with my own teams as we’ve adopted tools like GitHub Copilot, Cursor, Claude Code, Bolt, and other AI development tools. I require engineers to master these tools, but never as a substitute for engineering judgment. The agent can generate. The engineer must specify, verify, refactor, document, and own the result.

The session focuses on the practices that separate AI-assisted coding from responsible agentic software development: requirements interrogation before generation, clear task boundaries, verification against actual system behavior, refactoring before review, architectural ownership, simplicity, and deep product understanding. These disciplines keep AI-generated work from becoming dark code: code that looks polished, passes tests, and enters the codebase without anyone fully understanding what it does, why it was built that way, or what it touches.

I’ll also show how this connects to the rise of Product Engineers. In an AI-accelerated environment, the most valuable engineers aren’t just faster coders. They understand the product, the domain, the quality attributes that matter, and the architecture that must survive long after the demo works. They work alongside Product Managers to shape PRDs, clarify requirements, reduce or eliminate detailed user stories, and connect business goals directly to engineering execution.

For engineers, engineering leaders, architects, and product-minded technical professionals, this session offers a practical roadmap for staying valuable in the age of AI. The future belongs to engineers who can guide agents, protect the architecture, build technical wealth, and turn cheap code generation into durable business value.

Target audience:
1. Software engineers using or preparing to use AI coding tools
2. Senior engineers responsible for architecture, maintainability, and code review
3. Engineering managers and directors leading AI-accelerated development teams
4. Product Engineers working closely with Product Managers and Designers
5. Software architects responsible for preventing architectural drift
6. Technical program managers responsible for delivery outcomes, software quality, and engineering coordination

Preferred Session Duration: 50 mins including Q&A