Tech Debt Can't Wait for a "Gardening Week"

Every codebase carries the weight of its history. Quick fixes, temporary workarounds, and deadline compromises accumulate into tech debt, and like financial debt, the longer you ignore it, the more it costs. Full system rewrites to escape that debt are time-consuming and expensive, often only achieving parity with what already exists.

The common strategies for managing tech debt don't hold up. Dedicated "gardening weeks" treat the problem as an exception rather than a part of normal maintenance, which means large issues get deferred while small fixes are endlessly postponed. Padding feature timelines to accommodate debt work leads to unrealistic estimates and erodes trust between engineering and product. The core issue is that tech debt is treated as an interruption to the development flow, not an ongoing part of it.

Delegating Debt to the Coding Agent

The coding agent in GitHub Copilot offers a way to shift that paradigm. Instead of letting debt items die in the backlog while you build new features, you can assign issues directly to the agent. It works in parallel with your regular sprints, tackling the tedious work without disrupting your delivery commitments.

The GitHub billing team has used this approach for real, routine changes that otherwise might never have been prioritized. For example, the agent can be tasked to:

  • Increase test coverage in specific areas, taking over the time-consuming work of writing new tests.
  • Swap out dependencies, such as migrating between mocking libraries, without needing your full attention.
  • Standardize patterns, like unifying error return and logging conventions across the codebase to simplify future debugging.
  • Optimize frontend loading by changing the app to make API calls only when data is requested, instead of on every page load.
  • Find and eliminate dead code, including unused functions, outdated endpoints, and stale config.

These are the kinds of changes that used to take weeks of intermittent, split focus. With the agent, the time investment shrinks to a few minutes writing a clear issue and a few hours reviewing the pull request it produces. This isn't about replacing engineers; it's about delegating the repetitive refactoring and dependency updates so humans can focus on architecture, innovation, and complex business logic. The result is healthier software, faster shipping, and engineers spending time on work that requires their actual judgment.

The Human Element of AI-Driven Maintenance

AI agents are great at well-defined, repetitive tasks, but they don't understand the business context behind your code. That's where human oversight becomes critical. Every prompt needs careful consideration, and every code change requires thorough review.

Think of the agent as a partner that writes clean code all day without getting tired, but needs guidance on what matters for your specific application. It brings speed, consistency, and the ability to maintain focus across hundreds of changes. You bring the strategic thinking: deciding which debt to tackle first, understanding the business impact of different approaches, and recognizing when a "quick fix" might create larger problems later. The value comes from the interaction between your judgment and the agent's execution—you set the constraints and validate the solution, it handles the implementation details.

Getting the Most Out of the Coding Agent

Experience using the coding agent in GitHub Copilot for daily work points to a few practical lessons:

  1. Set up Copilot Instructions for your repository. The agent performs noticeably better when it knows your development scripts for formatting and linting (like go fmt and similar). You can even ask the agent to write these instructions for you.
  2. Scope issues in digestible chunks. If you are not specific about which areas of the codebase to change, the agent will make sweeping edits. Reviewing a 100-plus-file pull request is not a good use of anyone's time. Instead of one broad issue for "improve test coverage," create multiple issues focused on "file X" or "folder Y." The changes are easier to scope and far easier to review.
  3. Be specific in your prompts. The quality of the output depends on how well you communicate your context, constraints, and coding standards.
  4. Treat code review as mandatory. The agent doesn't understand business logic the way you do. Integrating the review into your standard workflow ensures quality while still benefiting from the automation. One advantage of the coding agent is that it works with the same review tools you use for colleagues' code, so it fits seamlessly into your existing practices.

Effectively collaborating with AI agents—mastering clear prompting, thoughtful review, and strategic delegation—offers a significant advantage. It becomes possible to maintain codebases others struggle with, tackle debt that typically gets deferred, and potentially avoid costly rewrites down the line. The technology is ready to be integrated into your workflow now, and the payoffs—for your codebase's health, your users' experience, and your own focus on the creative side of engineering—are worth the experiment.