Assess Current Process: — Identify areas where AI-assisted code generation is impacting the existing engineering workflow.
2
Define Architecture Requirements: — Establish clear architectural guidelines that promote agent-friendliness, such as explicit boundaries and stable contracts.
3
Adjust Code Review Process: — Modify code review procedures to focus on system behavior, security, and data integrity, rather than style issues.
4
Update Testing Standards: — Implement more rigorous testing standards, including regression coverage and edge-case assertions, for generated code.
5
Refine Hiring Criteria: — Adapt hiring and evaluation signals to prioritize skills like decomposition, acceptance criteria definition, and prompt engineering.
6
Implement Changes: — Roll out the updated processes, architecture, and standards to the engineering team.
7
Monitor and Iterate: — Continuously monitor the effectiveness of the changes and iterate based on feedback and observed results.
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