AI that Codes: How to Use Coding Agents Without Creating Technical Debt
Coding agents can now work in the background, open pull requests, and solve software tasks. But without review, tests, limits, and good practices, they can multiply technical debt instead of reducing it.
Code assistants have evolved rapidly. First, they completed lines. Then, they wrote functions. Now, coding agents can receive a task, analyze a repository, modify files, open a pull request, and wait for review.
GitHub introduced the Copilot coding agent in May 2025 as an asynchronous agent integrated into GitHub and accessible from VS Code. GitHub's documentation allows assigning issues to Copilot or asking it to create pull requests. Gartner, for its part, includes AI-native development platforms among its strategic trends for 2026.
This changes the way software is built, but it doesn't eliminate engineering. In fact, if used incorrectly, AI can produce code quickly and accumulate technical debt even faster.
What tasks fit well
A coding agent works best when the task is scoped and the repository has clear patterns.
Good cases:
- Fixing reproducible bugs
- Creating tests for existing behavior
- Refactoring a small function
- Updating dependencies with simple changes
- Implementing a screen following existing components
- Adding validations
- Improving technical documentation
- Creating internal scripts
Bad cases:
- Redesigning architecture without criteria
- Changing ambiguous business rules
- Touching critical security areas without supervision
- Large migrations without a plan
- Optimizing performance without data
- Creating general abstractions without necessity
The rule is simple: if you cannot explain the task in a clear issue, you probably shouldn't delegate it to an agent.
The real risk: code that compiles but doesn't fit
AI often produces code that looks correct. The problem is whether it fits with:
- Existing architecture
- Team style
- Business rules
- Security
- Performance
- Accessibility
- Tests
- Future maintenance
A junior human can also make these mistakes. The difference is that an agent can generate much higher volume in less time. Without review, the problem scales.
Define working rules for agents
Before using coding agents seriously, document how they should work.
Include:
- Installation, test, and build commands
- Component style
- Folder conventions
- Security policies
- Which files it must not touch
- How to name branches and pull requests
- Acceptance criteria
- Test requirements
Formats like AGENTS.md, repo instructions, or internal documentation help the agent have context. If your team already centralizes process documentation in tools like Polp, you can use that knowledge to convert repeated best practices into clear development instructions.
Review it as if it were a normal pull request
The worst mistake is assuming that "the AI did it" means it can be merged faster. It should be the opposite: initially, it is best to review with more care.
Review checklist:
- Does the solved task match the issue?
- Are there changes out of scope?
- Were unnecessary files modified?
- Do the tests cover the case?
- Are there visual or functional regressions?
- Are there security risks?
- Is the project style maintained?
- Is the code simpler or more complex than before?
AI must not skip the quality process. It must work within it.
Tests: the minimum barrier
Coding agents are much more useful in repositories with good tests. Without tests, the agent operates almost blindly, and the human reviewer has to validate too much manually.
Prioritize:
- Unit tests for logic
- Integration tests for APIs
- Tests for critical components
- Lint and type checks
- Mandatory build before merge
- Visual captures or tests in the frontend where applicable
A good flow is to first ask the agent to write a failing test, review if it captures the problem well, and then ask for the implementation.
Security and secrets
A code agent can read a lot of context. Therefore:
- Do not expose secrets in the repository
- Use environment variables correctly
- Limit token permissions
- Review new dependencies
- Prevent the agent from changing CI/CD configurations without review
- Protect main branches
Agents should not have direct write permissions to production. They must go through pull requests, checks, and review.
Cost per task, not cost per token
In development, the relevant cost is not just how much the model charges. It is how much it costs to solve a complete task.
Include:
- Issue preparation time
- Tokens consumed
- Review time
- CI failures
- Rework
- Subsequent bugs
A cheap agent that generates mediocre PRs can be expensive. A more expensive one that solves scoped tasks with good quality can save real hours.
Where it helps SMEs the most
For an SME, coding agents are especially useful in:
- Internal software maintenance
- Operational automations
- Administration dashboards
- API integrations
- Tests that were never written
- Project documentation
- Small migrations
They do not replace a technical team. They help the team deliver more, provided that someone with technical judgment sets the direction and reviews the work.
How we can help
At Navel Digital, we use AI in development with discipline: clear issues, prepared repositories, tests, human review, and controlled deployments. We also help companies create custom software and automations where AI accelerates work without turning the code into a black box.
AI that codes does not eliminate technical debt. It accelerates or reduces it depending on the working system you have in place.