Your AI needs a control plane

AI is remaking the entire software lifecycle. Agents write code, prompts steer behavior, and model updates change your product without a deploy. That’s exciting. But also terrifying.

What used to be a predictable merge → build → release flow now includes non-deterministic systems, live models, and fast-changing prompts. You need a runtime safety and experimentation layer that keeps pace.

That’s what Unleash provides: an AI control plane that lets platform and product teams change behavior safely at runtime, test ideas with real users, and roll back instantly when something goes wrong.

Why an AI control plane

Developers now work with copilots in their IDE, PR review bots in CI, and task-oriented agents that scaffold services, write tests, and propose migrations. These tools bring huge productivity gains, but they also introduce new challenges around governance, stability, and runtime control.

Traditional governance and pre-deploy checks aren’t enough. You need runtime control to:

• Progressively release new AI-assisted code or AI features with a limited blast radius.
• Target access by cohort, account, or region to respect policy and contracts.
• Roll back instantly when features misbehave or costs spike.
• Run experiments and make data-driven decisions safely.

Unleash has always focused on safe, progressive delivery by decoupling deployment from release. These same FeatureOps principles map perfectly to the world of AI systems and agentic workflows.

Ship safely with progressive delivery

Modern software delivery doesn’t stop at deployment. It extends to what’s happening live in production. With Unleash, you can:

• Start small: Release to 1–5% of traffic and monitor the impact before scaling.
• Target specific groups: De-risk rollouts by exposing new features only to internal users, beta testers, or specific regions.
• Roll back instantly: Use a kill switch to disable features immediately if metrics go off track.

Every deployment introduces risk, whether it’s a new algorithm, a database migration, or a simple UI tweak. Wrapping changes in a feature flag lets you:

• Merge and deploy continuously, even with incomplete features.
• Test safely in production before a full rollout.
• Separate delivery from exposure so a bad release never becomes an outage.

Yousician proves how powerful that approach can be. After running more than 600 full-stack experiments, their team has shown that flagging every feature isn’t just about safety—it accelerates learning. By wrapping every change in a feature flag, they can integrate early, deploy continuously, and release faster—with data to back every decision.

Measure what matters

AI introduces new forms of uncertainty, from non-deterministic behavior and model drift to unpredictable cost spikes. With Impact Metrics, Unleash surfaces the data you care about—error rates, adoption, latency, conversion, and infrastructure costs—into a single view. You can see what’s happening in production and correlate rollouts with business and engineering KPIs.

As Unleash CTO Ivar Østhus explained at UnleashCon:

We need to measure the impact across the entire stack.

It’s not enough to only observe the error rate or performance.

We also need to validate the impact on the business—and that we implemented it in a way that makes sense for our users.

When you pair impact metrics with release plans, you can:

• Define thresholds for success and progress releases based on data.
• Pause a release if metrics degrade.
• Correlate rollouts with customer, business, and engineering outcomes.

Keep latency low and uptime high

Your AI control plane must never become a bottleneck.

As Kirti Dhanai, Site Reliability Engineer at Wayfair shared at UnleashCon, Wayfair’s scale and AI-assisted development demanded a new level of control:

We’re living in the age of AI, and at Wayfair code velocity has exploded. Developers are pushing huge amounts of AI-assisted code—faster than ever.

But here’s the paradox: speed is up, reliability is down. To keep up, we had to adopt FeatureOps as a methodology.

The DevEx team at Wayfair manages more than 9,000 feature flags across thousands of services. Even tiny changes—a recommendation tweak or a checkout update—can ripple across millions of customers. They replaced their in-house feature toggle system with Unleash to scale feature management safely:

We moved from an in-house tool to Unleash, and out of the box it gave us performance, scalability, and control.

They run Unleash with Edge nodes distributed across their compute clusters, caching flags close to users to ensure low latency and high resilience.

During Black Friday traffic spikes, our feature-toggled system doesn’t even blink. Failures never become outages for us.

The results speak for themselves: latency under 5 ms, stable performance under 20,000 requests per second, and a threefold cost efficiency improvement over their previous setup.

Governance by design

Speed without control isn’t progress. It’s chaos.

Unleash builds governance into the runtime fabric, not as an afterthought:

• RBAC and audit logs: Track who changed what, when.
• Approval workflows: Enforce reviews for sensitive flags or production environments.
• Feature lifecycle management: Track every flag from definition to archive and prevent technical debt.

Teams like Prudential use these capabilities to scale governance across global engineering orgs without slowing delivery.

As our new platform moves, people are moving faster. As agentic and AI assistance start coming online, teams are asking for, ‘I want to release features more—how do I not break stuff?’ That’s exactly why we brought Unleash into the stack.

– Peter Ho, VP DevOps at Prudential Financial

Feature Ops for GenAI infrastructure

Unleash’s architecture makes even AI infrastructure governable.

You can move model names, prompt templates, and parameters out of code and into configuration managed at runtime—treating them as first-class objects you can roll out, compare, and roll back without redeploying.

At ASAPP, a GenAI customer service platform serving enterprise call centers, Unleash powers both software and ML deployment workflows.

We use Unleash across multiple Kubernetes clusters to manage customer-specific deployments.

The full-stack FeatureOps approach is critical for us to safely deploy our code, our models, and our prompts.

 

– Ivan, Principal Platform Engineer at ASAPP.

By treating models, prompts, and configurations as flag-controlled runtime elements, ASAPP’s platform teams maintain consistency across multi-tenant clusters while giving data scientists safe autonomy to experiment.

Experimentation became a much bigger part of our product development strategy.

Unleash made it really easy to adopt experimentation across multiple languages—with guardrails in place.

A practical rollout workflow

Here’s how these principles come together in a typical feature release:

• Flag it: Wrap any new feature or code changes in a feature flag.
• Target it: Start with a limited release—like 5% of eligible users in a single region.
• Monitor it: Track the metrics you care about, such as latency, adoption, infrastructure cost, and error rates. If metrics stay healthy for a set period, progress to 25%. If something spikes, pause for review.
• Decide: Promote to 100% or roll back instantly based on the data.
• Clean up: Once the release is complete, retire the old flag and remove dead code paths.

This workflow ensures every change, human-coded or AI-generated, moves safely from idea to impact.

The payoff: velocity, resilience, and confidence

AI is rewriting how software gets built—but without runtime control, it’s a gamble.

Unleash gives teams the power to move at AI speed without losing trust, stability, or control. Implementing an AI control plane gives teams higher velocity with lower risk by decoupling deployment from release.

Across industries, teams like Prudential, Wayfair, Yousician, and ASAPP prove the pattern:

Wrap every change, model, or prompt in a feature flag, measure the impact, and ship with confidence.

The companies that win in the AI era won’t just build faster. They’ll learn faster—because their control plane makes every release an experiment, not a risk.