Everyone knows that AI has changed how software gets built, but it’s also changed what can go wrong after it’s been shipped. There are two reasons for this shift.
The first is speed. AI lets teams write more code in a fraction of the time, and the human-driven review process can’t keep up. The second—which is specifically relevant to AI-powered features—is unpredictability. The underlying models are updated regularly (and silently) by third-party providers, and context can shift over time. Either change can cause a previously reliable prompt to start producing bad output, and these failure modes are difficult to detect.
The teams who are feeling this pressure most acutely are the ones that still treat deploy and release as the same event. Deploying is putting code on a server. Releasing is controlling who's exposed to it, and for how long. When those two collapse into one moment—or when the controls you have in place don't run deep enough—preproduction validation becomes your last line of defense. This approach was always risky, but in the AI era, that risk is unsustainable.
If this sounds like you, check out these five signs your release process is behind the times.
Sign 1: Human reviewers have become the bottleneck
AI coding agents and copilots make changes faster than any team can review them, and a single PR might touch multiple files, services, and data models. Reviewers who used to thoroughly vet all new code now find themselves with barely enough time to skim it.
This problem is compounded by the fact that agents lack robust institutional knowledge. For instance, a human engineer knows the details of an incident that happened six months ago, or why unusual workarounds exist in the codebase, and that awareness informs their approach. When an agent writes code, the reviewer is left to reconstruct this missing context alone, which makes the agent's work harder to trust.
This issue clarifies why a modern, AI-ready release process is so crucial: Review can no longer be the only checkpoint between a change and your customers.
Sign 2: When you deploy a change, you release it to all customers at once
When there isn’t enough time to thoroughly review every AI-generated change, teams have to make difficult decisions when a deadline hits. The LaunchDarkly Control Gap Report quantifies this trade-off, with 82% of teams reporting that they’ve shipped changes with unresolved risk in the past six months because they were under pressure to deliver. When deploy and release are treated as the same event, these problematic changes hit every customer at the same time.
This approach is especially dangerous when it comes to deploying AI-powered features because a bad change doesn't always misbehave the same way for everyone. For instance, a model might only produce bad output for certain inputs or certain users, and this kind of variance is easy for a system-wide dashboard to average away.
Teams that have solved this problem haven’t created a foolproof review gate that guarantees every AI-generated change is safe; they’ve simply stopped treating merge as the only decision point. For these teams, a change rolls out in phases, starting with a small percentage of real traffic or one customer segment and expanding only as confidence builds. From there, the team monitors metrics tied to that specific change, and a regression can be correlated with the exact audience it was exposed to.
Sign 3: You can’t correlate an incident with the exact change that caused it
When an incident occurs in production, the investigation process typically begins with the same question: What changed right before the issue started? That question can be answered relatively quickly when there’s a short list of suspects, but the process of sifting through telemetry data and testing each change manually doesn’t hold up at AI-generated code volumes. Issues with AI-powered features are even harder to investigate because there’s no diff when a model provider is updated or a prompt drifts.
A scalable solution depends on two things. The first is the ability to tie every change—whether it's a change to code or a shift to a new model or prompt version—to the metrics collected while it was live. The second is the ability to revert that specific change automatically at runtime the moment something goes wrong.
Sign 4: The fix for a bad change is a full redeploy
Knowing which change caused an incident can only get you so far when the only way to act on it is to write and deploy a fix. This process is slow, and the bad change remains live and in front of customers until the redeploy is complete. The resulting pressure often pushes teams toward a fast patch instead of a durable fix.
For AI-powered features, there are even fewer options. If the cause is a model update pushed by a provider, there's no bug in your code and no patch to write because the problem isn't in your repo at all. A redeploy won’t help; the only option is to turn the entire feature off.
AI-native teams don’t rely on full redeploys to fix customer-facing issues. Instead, they have tooling in place that allows them to turn off a bad change in place the moment they find it. A code-level issue gets fixed on the team's own timeline, and a bad prompt or model change reverts to the last known-good version automatically.
Sign 5: Engineers spend more time fighting fires than they do building
The fifth sign is the easiest to miss because it often masquerades as a staffing problem, but it has the same root cause as the other four.
A review process that can't keep pace lets issues sneak into production, and a single premerge check means there's no emergency brake when something goes wrong. An incident with no clear cause adds hours of correlation before remediation can even start, and a fix that requires a full redeploy keeps the bad change live through investigation and a rewrite.
These issues compound over time, pulling engineers away from the roadmap over and over again. And they can’t be solved by adding more engineers or doing more review. Instead, they require a closer, more holistic look at your release process.
Teams that have figured this out have moved control to production—regardless of whether they're using AI to write code, shipping AI features in production, or both. That shift requires a new operating model, and it’s called runtime control. See how it works.
