AI Needs a Permanent Babysitter

One of the biggest problems with modern AI coding is that models still need constant supervision practically all the time. Generating code is not really the difficult part. The problem appears when the agent needs to maintain consistency, understand project context, follow existing patterns, and avoid absurd decisions while working on real systems. That is where it becomes obvious that AI still does not really know how to work alone.

AI often looks competent for a while, especially in small or isolated tasks. But the longer it works on a real system, the more strange behaviors start to appear. It changes patterns for no reason, removes valid logic because it “simplifies” the code, breaks existing components to eliminate quick warnings, or introduces superficial fixes that technically work but slowly degrade the entire system. Many times it even ignores explicit instructions that had already been corrected several times before.

And probably one of the most frustrating things is that it needs everything repeated constantly. You explain an architecture decision. Then you explain it again a few interactions later because the model completely forgot it. You correct an incorrect pattern and later it goes back to the exact same behavior. You tell it not to touch a certain part of the system and it modifies it anyway. A large part of the workflow ends up becoming constant supervision, continuous correction, and repeated context over and over again.

A meaningful part of the problem is that AI does not really understand consequences the way a human developer does. Many times it detects something that “looks incorrect” and decides to modify it even if that makes the whole system worse a few hours later. The model does not understand project history, technical tradeoffs, accumulated decisions, or the reasons behind certain patterns. It simply generates the next prediction that looks statistically reasonable.

And that is probably why so many systems are starting to appear around current models. Memory systems, orchestration layers, reinforcement systems, guardrails, structured guidance, and context injection exist precisely because AI cannot be fully trusted to work alone during long workflows. A large part of the modern AI ecosystem has stopped focusing only on making models more capable. Now a large part of the work is building systems that reduce chaos, inconsistency, and destructive decisions while the model works.

A meaningful part of a11y-engine was born precisely around that idea. The Intelligence system does not exist because AI cannot generate fixes. It exists because AI cannot be left alone while generating them. That is why the engine transforms raw findings into structured remediation data. Each rule can include framework notes, validation reminders, guardrails, related rules, and additional context to help agents produce less destructive and more consistent fixes during real accessibility workflows.

And that is probably one of the most important realities of AI coding today. The more complex the project becomes, the more obvious it becomes that AI still needs a permanent babysitter. Not because it cannot generate useful code, but because it still does not really understand the consequences of many of the decisions it makes while working alone.