AI Coding Agents: A Week of Real-World Engineering Data

I used AI coding agents for a week at work. Here is what actually happened.

Emily Woods integrated AI agents like Cursor and Claude Code into her full-stack workflow for one work week to test production efficiency. While raw code volume increased by 40%, the volume of logic shipped to production without significant rework remained roughly the same.

Why This Matters

The reality of engineering involves navigating organizational memory and operational constraints that AI agents currently cannot access. Relying on textbook recommendations for architectural decisions—such as adding a cache—fails when agents lack context regarding team on-call rotations or pending upstream optimizations, leading to technically correct but practically wrong solutions in a production environment.

Key Insights

• Structural boilerplate generation: Cursor generated 80% of a Python/Go service including Kafka consumers and SQLAlchemy layers in 12 minutes (2026).
• Automated test coverage: Claude Code produced a solid unit test suite for an existing service in 20 minutes, identifying missed boundary conditions.
• Contextual failure: AI agents failed to resolve production incidents involving schema changes because the root causes were documented in Slack threads and Git history, not just the active codebase.
• Documentation efficiency: Claude Code analyzed a six-month-old service to generate 90% accurate architectural walkthroughs for onboarding new teammates.
• Business logic regression: Implementing complex billing proration resulted in a 90-minute cycle of agent-driven regressions, ultimately requiring manual human intervention.

Practical Applications

• Use Case: Leveraging Claude Code for rapid test suite expansion on legacy services. Pitfall: Assuming database mock layers are correctly understood, which required manual adjustment of 15% of tests.
• Use Case: Using Cursor for predictable structural patterns like new service scaffolding and Pydantic models. Pitfall: Blindly trusting output without review, which would have caused at least two production incidents in one week.

References:

• https://dev.to/emilywoodsnyc/i-used-ai-coding-agents-for-a-week-at-work-here-is-what-actually-happened-99h