Coding agents are booming. Only the lazy haven’t yet talked about how they built their own agent. But the reality is much more complex. To get the benefits from AI your development ecosystem should be ready for it.
Garbage in -> garbage out, remember?
Low coverage, flaky tests, undefined code style, long verification cycle, poor documentation. Add AI-generated code to this and you will increase the entropy and reduce overall system stability.
To produce predictable results the engineering infrastructure must be stable.
By
infrastructure I mean:🔸 Linters and automated code style verification.
🔸 High unit tests coverage (>=80%).
🔸 Contract tests for all public APIs.
🔸 System, integration, and E2E tests that run at least once a day.
🔸 No flakiness. You must fully trust your tests and CI process otherwise you cannot guarantee that agent won't break anything.
🔸 Security gates. Secret management, vulnerability checks, SAST verification.
🔸 Documentation. Requirements, architecture, guides, internal agreements. Everything that helps the agent understand how we work.
The most non-obvious part here is test flakiness.
What's the problem with just rerunning the test?
Developers know the context, the agents do not. It means that they will try to fix the test, making it weaker, or modify the code, introducing a bug. The overall result is worse code generation and increased maintenance overhead. So each rerun must be treated as a bug report, not a solution.
If you check how different companies adopt AI, you can notice that all success stories are based on existing powerful CI\CD processes that can safely check AI agent output (Google, Claude Code, Uber, Google, Airbnb).
AI adoption doesn't just bring new tools and processes but also forces the best engineering practices we already have.
#engineering #ci #ai #agents