What to look for in an AI control plane

AI code generation is changing how software gets built. The State of DevOps report shows 90% of developers use AI coding assistance daily, and Gartner projects that by 2028, most code will be AI-generated. The productivity gains are real, but so are the risks.

A 25% increase in AI adoption correlated with a 7.2% drop in delivery stability. AI speeds up code reviews by 3.1% and approvals by 1.3%, meaning bugs reach production faster. A 2025 study of 500,000 code samples found that AI-generated code carries significantly more high-risk security vulnerabilities than human-written code.

The challenge: capture productivity gains without trading reliability for speed. The answer is implementing controls at the right points in your delivery pipeline.

Why tool standardization fails

Some organizations tried mandating a single AI assistant. This fails for three reasons: the market moves too fast (Gartner lists 14 assistants and growing), different tools excel at different tasks, and developers already have preferences. Enforcing a single choice creates friction without addressing risk.

The better model: let teams choose their tools while enforcing controls at specific chokepoints. This follows BYOD policies and cloud adoption. You set requirements for how tools integrate with enterprise systems, not which tools people use.

Two control points that matter

For AI-generated code, enforce centralized control at two points: continuous integration and runtime.

Control point 1: Continuous integration

Organizations already use CI to enforce non-negotiables: static analysis catches security flaws, dependency scanners block vulnerable packages, license checks prevent compliance violations. The same pipeline can mandate that behavioral changes be wrapped in feature flags.

Define “behavioral change” broadly. It’s not just user-facing features. Internal logic changes, configuration updates, and backend validation paths all qualify. Both the Google Gmail outage in June 2025 and the Cloudflare incident in November 2025 were caused by routine backend changes that lacked runtime controls. Google’s postmortem: “The issue with this change was that it did not have appropriate error handling nor was it feature flag protected.”

Implementation varies. Some rely on PR reviews, others use automated linting. The principle matters more than the mechanism: if a change affects system behavior, it needs runtime control. This integrates naturally with trunk-based development, where teams merge to main frequently while keeping incomplete features hidden behind flags.

Control point 2: Runtime

Feature flags control code behavior without redeploying. Enable a feature for a small percentage of users, monitor for issues, roll back instantly if needed. The difference between a five-minute recovery and a five-hour outage often comes down to whether you can disable a feature at runtime or need to roll back an entire deployment.

Runtime control enables gradual rollouts: expose new code to progressively larger cohorts while monitoring metrics. If AI-generated code introduces a subtle bug that only manifests under specific conditions, you want to catch it when it affects 5% of traffic, not 100%.

Core capabilities of an AI control plane

An AI control plane governs AI-generated code through explicit, enforceable rules without dictating development practices. That requires specific capabilities.

Centralized policy enforcement.

Every change passes through the same checkpoints. CI pipelines verify flags exist, runtime systems enforce access controls and approval workflows. Role-based permissions prevent AI agents from bypassing enterprise rules, audit logs track changes. For critical environments, change requests enforce four-eyes approval before changes reach production.

Runtime governance across services.

Feature flags must work consistently across monoliths, microservices, on-premises, and cloud. The Unleash architecture uses a centralized API with SDKs that handle flag evaluation locally—user data never leaves your application.

Instant rollback capability.

Kill switches and feature-level rollbacks contain failures in seconds, not hours. Critical with AI-generated code, which can introduce bugs that only surface under specific conditions.

Flag lifecycle management.

Each flag needs an owner, a release plan, and success criteria. Unleash provides lifecycle stages to identify and retire flags, preventing sprawl and technical debt.

Auditability and compliance.

Track who made what change, when, and through what approval process. Essential for regulated industries where every production change is a compliance event.

Implementation requirements

Getting this right requires following established best practices for feature flags. The technical implementation needs to support certain patterns:

• Evaluate flags high in the stack. Check the flag once at the controller or component level and pass the result down. Don’t scatter flag checks throughout your codebase. This keeps your code testable and makes cleanup easier when you retire the flag.
• Use consistent naming conventions. Flags need unique, descriptive names. A naming scheme like [team]-[feature-name] makes flags searchable and clarifies ownership. Store flag names in a centralized file so they’re easy to find and remove.
• Handle both paths. While a flag is active, test both the enabled and disabled code paths. Don’t let the old path rot while you’re testing the new one.
• Remove flags after rollout. Temporary flags should have an expiration plan. Once a feature is fully rolled out and stable, remove the flag and delete the old code path. This prevents the codebase from becoming littered with dead switches.
• Minimize payload size. In distributed systems, flag configurations are often cached in memory. Keep payloads small to avoid memory exhaustion and reduce network overhead.
• Organize flags strategically. Structure your projects and environments to match how your teams work, with clear ownership boundaries and environment-specific permissions.

Real-world implementation

Prudential Financial uses Unleash for standard releases and AI-assisted development. With thousands of developers and strict compliance requirements, every feature change is tracked as a production event.

Wayfair followed a similar pattern. Kirti Dhanai from Site Reliability: “Developers are pushing huge amounts of AI-assisted code. Speed is up, reliability is down. To keep up without breaks in production, we had to adopt FeatureOps using Unleash.”

Both solved the same problem: capture productivity gains without sacrificing reliability by enforcing controls where code reaches production.

Key capabilities to evaluate

When you’re evaluating systems to serve as your AI control plane, these are the capabilities that matter:

• Does it integrate with your existing CI/CD pipeline to enforce flag coverage?
• Can it handle runtime governance across all your services and environments?
• Does it provide instant rollback without requiring redeployment?
• Can it scale to thousands of developers and millions of flags?
• Does it support the approval workflows and access controls you need for compliance?
• Will it track the full lifecycle of every flag to prevent technical debt?

None of this is theoretical. Feature flags are already a proven practice, endorsed by Martin Fowler and ThoughtWorks, recommended in Google’s Site Reliability Engineering handbook, and included in AWS’s Well-Architected Framework. What’s changed is the risk profile. AI has fundamentally altered the volume and velocity equation.