AI Agent Governance and Developer Enablement
January 2026 – PresentCompuGroup Medical (CGM)
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.