How to Deploy AI Agents in Legacy Code to Avoid Silent Regressions in Production

A translated article on Habr discusses how to implement AI agents into legacy code in a way that prevents silent regressions — errors that aren't immediately visible but break the system later.

Where AI Agents Stumble

An AI agent is capable of refactoring a module, moving a component, or cleaning up a chunk of code in a few minutes — this is precisely the routine work that teams have lacked time for over years. Problems begin when implicit contracts surface in the system — old integrations, business rules, workarounds, and dependencies that the model simply doesn't know about. The agent doesn't see the context that the team has held in their heads for years rather than in documentation, and therefore can break what appeared at first glance to be an innocent refactoring.

According to the authors, this is precisely where the main danger lies: the agent doesn't fail obviously or crudely — it produces code that passes surface-level checks, compiles, and looks cleaner than before, but quietly removes behavior that someone in the system relied upon.

How Not to Revert the Refactoring

The article's authors advise setting up a safe perimeter in advance rather than relying on the model to figure out the project history on its own. This means tests that fix current system behavior before an agent is let loose on it, documentation of implicit contracts, staged migration instead of one-off large-scale changes, and clearly defined boundaries for acceptable compromises that the agent cannot cross.

• An AI agent can refactor a module or move a component in a few minutes
• The main risk — implicit contracts: old integrations, business rules, workarounds, hidden dependencies
• Recommendation — set up tests, documentation, migration phases, and boundaries for acceptable compromises in advance
• The goal — so the agent speeds up development rather than reverting a 4000-line refactoring

Why This Concerns More Than Just Giant Systems

The problem of implicit contracts is relevant not just for massive corporate monoliths. Even in a medium-sized project over time, solutions accumulate whose meaning is explained by one comment in an old commit or nowhere at all — and such places are often precisely the implicit boundary that cannot be violated during refactoring.

Who Is Responsible for Controlling the Agent

From the article's logic, responsibility for safe AI agent implementation lies not with the model itself, but with the team that connects it to the codebase. Tests, documentation, and boundaries for acceptable compromises are not optional insurance, but a mandatory part of the process, without which the speed the agent provides on simple tasks turns into risk on complex ones. In other words, the fewer explicit contracts a project has, the more preparatory work must be done before letting an autonomous agent loose on it.

What This Means

Implementing AI agents in legacy systems requires not so much trust in the model as engineering preparation: without tests and explicitly described contracts, the agent risks breaking what has been held together for decades through implicit agreements inside the team rather than explicitly written rules.

Essentially, the article describes a general shift in requirements for engineering culture in teams that begin actively using AI agents: the more autonomous the agent becomes, the more important it is to formalize in advance what could previously be kept in one's head — and the more costly each gap in tests or documentation becomes, on which the agent will inevitably stumble. Teams that invest in such preparation upfront will get real development acceleration from AI agents rather than a new source of hidden bugs.