AI coding tools expose gaps in engineering rigor because they let teams generate more code than they can specify, verify, refactor, review, and safely own.

This talk explains why agentic coding raises the bar for engineering discipline across the full software delivery workflow. Teams need stronger preparation before generation, clearer verification standards after generation, more disciplined refactoring before review, and a higher bar for accepting AI-generated code into production. Without that rigor, agentic coding can produce large amounts of plausible output that looks productive while quietly increasing maintainability risk, architectural drift, and ownership burden.

The session gives CTOs, VPs of Engineering, and engineering directors a practical way to think about AI workforce development. Engineers need to learn how to interrogate requirements, define constraints and invariants, confirm that tasks are independently verifiable, check agent assumptions against actual system behavior, review for architectural fit, and own every meaningful change they merge. These are trainable engineering behaviors, and they need to become explicit standards as AI coding tools become normal parts of software delivery.

Attendees will leave with a clear model for identifying where their teams need more rigor before scaling agentic coding: specification, verification, refactoring, review, architecture, product understanding, and accountability. AI can accelerate execution, but engineering rigor determines whether that acceleration produces technical wealth or technical debt.

This talk directly references the author's Engineering Standards for Agentic Software Development, especially TD1, SP1, VT1, AM4, AC2, and DK1: https://www.linkedin.com/pulse/engineering-standards-agentic-software-development-edensoft-park-ki1se

Target audience: CTOs, VPs of Engineering, engineering directors, technical program managers, and executives responsible for AI workforce development.

Preferred Session Duration:
50 mins including Q&A