Agentic coding gives software teams a dangerous new capability: generating more code than their engineering discipline can absorb. The code may compile. The tests may pass. The demo may work. Under the surface, agents typically introduce duplicate logic, inconsistent patterns, unnecessary abstractions, architectural drift, and fragile design decisions that reviewers miss because the output looks polished and plausible.

This talk explains why AI-generated code changes the economics of technical debt. Before agentic coding, poor engineering decisions accumulated at human speed. Now they can accumulate at machine speed. Engineering directors and VPs need operating rules for how agent-generated code is specified, reviewed, refactored, and accepted into production.

The session introduces practical standards for preventing AI-accelerated debt: refactoring for architectural fit before review, checking for duplication before merge, challenging unnecessary complexity, and running recurring architectural drift scans across active codebases. It shows why local correctness is no longer enough and why engineering teams must treat maintainability, architectural coherence, and conceptual integrity as required outcomes of AI adoption.

This talk directly references the author's Engineering Standards for Agentic Software Development, especially RF1, RF3, RF4, AO4, and DN5: https://www.linkedin.com/pulse/engineering-standards-agentic-software-development-edensoft-park-ki1se

Target audience: Engineering directors, VPs of Engineering, CTOs, senior architects, and executives responsible for AI adoption across software teams.

Preferred Session Duration:
50 mins including Q&A