AI-enabled teams need a clear definition of done for agent-generated code. Without one, teams accept code because it works, passes tests, or came from a trusted tool. That standard is too weak for production systems. Agent-generated code needs a merge gate that verifies specification quality, constraints, system behavior, architectural fit, refactoring discipline, and engineer comprehension before it enters production.

This talk presents a practical merge gate for AI-generated pull requests. It covers the required evidence teams should expect in every agent-generated PR: specification summary, constraints and invariants, tests run, logs inspected, workflows exercised, architectural impact, refactoring performed, and known risks. It also explains why certain items should be treated as merge blockers rather than review preferences.

The session is designed to be immediately useful. Attendees will see how to turn AI coding standards into a lightweight, enforceable PR review practice. The goal is to help teams move quickly without lowering the quality bar, especially as agents increase the volume and apparent polish of submitted code.

Attendees will leave with a practical pattern for defining what “done” means when agents generate code: specification before generation, verification against actual system behavior, refactoring before review, architectural review before approval, and engineer comprehension before merge.

This talk directly references the author's Engineering Standards for Agentic Software Development, especially Appendix A, SP1, SP2, SP3, EM1, VT1, RF1-RF9, and AC2: https://www.linkedin.com/pulse/engineering-standards-agentic-software-development-edensoft-park-ki1se

Target audience: Engineering managers, DevSecOps leaders, software factory leaders, tech leads, and teams formalizing AI coding governance.

Preferred Session Duration:
50 mins including Q&A