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For developers navigating intricate infrastructure codebases, AI coding assistants promise accelerated workflows—but how do they fare in the trenches of database service development? Over six months, a senior engineer at a managed MySQL database-as-a-service provider rigorously tested GitHub Copilot (using Claude Sonnet) within their production environment, revealing nuanced insights beyond typical hype cycles.

The codebase—spanning thousands of lines across Azure resource management, high availability systems, and backup workflows—became a testing ground for AI assistance. When dependency conflicts erupted during an Azure library upgrade, Copilot's "Agent Mode" demonstrated unexpected prowess: "It analyzed build files and implemented a fix, isolating the updated dependency to my component while retaining older versions elsewhere," the engineer reported. This highlighted AI's advantage in pattern-based dependency resolution, where algorithmic analysis outperforms manual search in complex dependency trees.

Testing scenarios proved more contentious. While Copilot generated unit tests meeting 86% coverage requirements, it frequently hallucinated file structures and boilerplate. "It modified build files and added dozens of tests that failed compilation," noted the engineer, who intervened to redirect the tool with explicit path constraints. End-to-end testing revealed similar overreach, with initial outputs generating hundreds of lines of failing complex scenarios. The lesson? Start small: successful implementation required resetting prompts to generate basic "Hello World" tests before iterative expansion.

Where Copilot excelled was in knowledge retrieval and documentation. Faced with scattered high-availability logic spanning health detection and failover mechanisms, the assistant delivered comprehensive workflow breakdowns with code pointers. When asked targeted follow-ups like "How long until an unhealthy instance is detected?" it augmented explanations dynamically. Similarly, documentation tasks flourished: "It wrote a marvelous new-hire guide with intros, steps, and estimated timelines referencing existing resources," demonstrating AI's capacity as a force multiplier for knowledge sharing.

Refactoring monolithic code surfaced stylistic preferences—the assistant consistently favored nested methods and grouped return conditions—but succeeded when given clear constraints ("one method should do one thing"). This underscores the importance of philosophical guardrails during AI-assisted refactoring.

The engineer now positions Copilot as a "first responder" for technical questions, significantly reducing team interrupt costs. Yet the core workflow remains human-driven: feature code originates with developers, while AI handles improvements and supplementary tasks. As infrastructure complexity grows, this balanced approach—leveraging AI's pattern recognition while maintaining human oversight—may define the next era of systems development.

Source: Mazhar's AI Coding Assistant Retrospective