How to bring AI agents into legacy code without silent regressions in production

A translated article on Habr explains how to bring AI agents into legacy code without creating silent regressions: bugs that don't show up right away but break the system later.

Where AI agents stumble

An AI agent can refactor a module, move a component, or clean up a chunk of code in a couple of minutes. That's exactly the kind of routine work teams have lacked time for over the years. The trouble starts when implicit contracts surface in the system: old integrations, business rules, workarounds, and dependencies the model simply doesn't know about. The agent can't see context that lived in the team's heads for years rather than in the documentation, so it can break something that looked like a harmless refactor at first glance.

According to the authors, this is the main danger. The agent doesn't fail in obvious, crude ways. It produces code that passes a surface-level review, compiles, and looks cleaner than before, but quietly removes behavior that something else in the system relied on.

How not to roll back your refactoring

The authors advise setting up a safe perimeter in advance instead of counting on the model to figure out the project's history on its own. That means tests that lock in the system's current behavior before the agent gets access, documentation of implicit contracts, staged migration instead of a single large-scale change, and clearly defined boundaries for acceptable trade-offs that the agent must not cross.

• An AI agent can refactor a module or move a component in a couple of minutes
• The main risk is implicit contracts: old integrations, business rules, workarounds, hidden dependencies
• The recommendation: set up tests, documentation, migration stages, and boundaries for acceptable trade-offs in advance
• The goal: make the agent speed up development rather than roll back 4000 lines of refactoring

Why this isn't just about giant systems

Implicit contracts aren't only a problem for huge enterprise monoliths. Even a mid-sized project accumulates decisions over time whose rationale is explained by a single comment in an old commit, or nowhere at all. Those spots most often turn out to be the unwritten boundary you can't break during a refactor.

Who is responsible for controlling the agent

The article's logic implies that responsibility for deploying an AI agent safely lies not with the model itself but with the team connecting it to the codebase. Tests, documentation, and boundaries for acceptable trade-offs aren't optional insurance; they're a mandatory part of the process. Without them, the speed an agent delivers on simple tasks turns into risk on complex ones. In other words, the fewer explicit contracts a project has, the more prep work it needs before you let an autonomous agent near it.

What this means

Bringing AI agents into legacy systems calls less for trust in the model than for engineering preparation. Without tests and explicitly documented contracts, the agent risks breaking things that for decades ran on unwritten agreements within the team rather than on explicitly defined rules.

In effect, the article describes a broader shift in the engineering culture expected of teams that start using AI agents heavily: the more autonomous the agent, the more important it is to formalize in advance what used to live in people's heads, and the more expensive every gap in tests or documentation becomes, since the agent will inevitably trip over it. Teams that invest in this preparation up front will get a real development speedup from AI agents, not a new source of hidden bugs.