As AI coding agents became part of our daily work at CGM, their rapid
adoption exposed inconsistent quality, duplicated effort, unreliable
outputs, and the absence of common standards. In my role as a Software
Engineer with Senior Developer seniority, I responded by leading the
creation of a shared framework for the team.
I authored a governance model covering ownership, contribution and review
lifecycles, independent testing, preview rollouts, composability,
non-overlapping activation rules, and recommended quality thresholds. To
make evaluation repeatable, I designed a weighted scoring rubric and
implemented a deterministic Python tool that evaluates skills and
workflows, reports critical issues, and ranks improvements by impact.
I also built reusable agent skills for safe Kubernetes diagnostics, Jira
issue management, GitLab pipelines and merge requests, local Git
operations, feature-environment resolution, and automated quality
evaluation. Two orchestration workflows combine these capabilities into
structured troubleshooting and evaluation processes, with explicit
contracts, reusable dependencies, safety guardrails, and failure handling.
These assets are used broadly across the team. I continue to review
contributions, help colleagues create and debug their own workflows,
support them when they encounter AI-agent or tool-integration issues, and
evolve the framework based on practical feedback.