Where Cloud Feature Flagging Fails for Robotics Configuration Management

Introduction

As their fleet of robots grows, robotics teams look for scalable ways to manage, version, and deploy their configurations. Those with a cloud software background tend to first turn to cloud feature-flagging tools like LaunchDarkly, StatSig, or ConfigCat.

Unfortunately, these tools were not built with the complexities of robotics in mind. As a result, they fall short of solving configuration management in robotics.

This blog explores these different shortcomings.

Bidirectional Sync and Local Caching

Managing configuration in robotics is more complex than cloud apps because robots operate in dynamic, network-constrained environments. Their relationship with the server is different from that of cloud apps. Their propensity to lose network and power means they must be resilient to outages.

To handle this, robots need to be able to sync configurations bidirectionally, store them locally, and recover from failures autonomously.

Bidirectional Sync

When a robot’s sensors are recalibrated, those new values should sync back to the cloud to be versioned with the rest of the configuration. If a developer pushes new config values from the cloud, the system should merge those changes without erasing local updates. Local changes should take precedence when necessary, and conflicts should be resolved automatically.

Local Caching and Offline Recovery

Robots operate in unstable network conditions, so they must store configs locally and handle failures without human intervention. To be resilient to network drops, the configuration should be cached, and polling should be retried once the connection is restored. If the robot reboots or loses power, it should revert to the last known working config and instantiate without relying on cloud access.

Why Push-Based Models Fail

Feature flagging tools are designed for cloud applications, not robotics. They rely on a push-based model where config updates are streamed from the cloud through a persistent connection (e.g., via server-sent events or WebSockets). This model assumes:

• A stable connection is always available.

• The cloud holds the single source of truth for config values.

• Clients don’t need to store config values locally.

This setup works fine for cloud apps but not for robots. If the connection fails mid-update, the robot will be left in an undefined state. Robots must store configs locally and recover autonomously, which the push-based model isn’t designed to handle.

State-Based Updates

Robotic systems are stateful. Updating a robot mid-task—like during heavy lifting or precise movement—can lead to mechanical failures or safety hazards. Unlike cloud apps, robots must carefully time configuration updates to avoid interrupting critical operations.

Cloud-based feature flagging tools rely heavily on Server-Sent Events (SSE). SSE streams data continuously, which works fine for stateless environments. But in robotics, state drives behavior. Applying updates as soon as they arrive can be dangerous.

To support this paradigm, robotics teams must write custom logic to monitor and manage state when applying updates — such as detecting whether the robot is active or at rest and deferring updates during critical tasks.

This custom software is an additional burden on their infrastructure team and, if improperly maintained, can cause critical path errors.

Hierarchical Overrides and Rollbacks

Feature flagging tools are built for user-based segmentation — but robots need complex, hierarchical overrides. Cloud-based tools assume you’re targeting users, not machines. That mismatch makes overrides and grouping harder than they should be.

Granular Targeting

Robotics configurations aren’t flat; they’re hierarchical. You might want to update motor tuning for all industrial AMRs—except the fleet in Factory A, where the customer’s safety requirements mandate different settings. Even within that fleet, robots aren’t identical. One robot might have a misaligned sensor due to manufacturing tolerances—you’d want to apply a correction only to that specific unit without affecting the rest.

Cloud feature flag tools struggle with this level of precision. They’re designed for simple user groups—like “all users in North America” or “beta testers”—not for multi-layered segmentation or per-device adjustments.

Fleet-Wide Rollbacks

Say you push an experimental update to 100 robots. Five robots experience issues—maybe due to mechanical wear or environmental differences—but the rest are fine. You’d want to roll back the update for those five without affecting the others.

Most feature flag tools rely on simple user-based groupings. The rollback options will be limited if the grouping model isn’t granular enough to distinguish between different robot states or environments.

Conclusion

Robotics teams face unique challenges in configuration management that cloud-based feature flag tools weren’t built to handle:

• Bidirectional sync – Robots need to merge local changes with cloud updates without conflicts, even when offline.

• State-aware updates – Config changes must account for real-time state to avoid destabilizing active tasks.

• Granular targeting and rollbacks – Robotics configurations are hierarchical and require precise control at the fleet, model, and unit level.

Flagging tools assume a stable connection and a stateless environment—neither of which apply to robots. To robotics teams looking for a way to solve this problem, you’ll have to keep looking for another solution.

Thanks for reading!

Fortunately, you won’t have to keep looking much longer :)

At Miru, we’re building configuration management for robotics. We help robotics teams easily manage, version, and deploy their application configurations. If this is a pain you’re facing, please reach out!