Rollbar powers AI-assisted workflows with new grouping engine
Rollbar, providers of the Continuous Code Improvement Platform, is introducing AI-assisted workflows powered by its new automation-grade grouping engine, enabling developers to spend less time monitoring, investigating, and debugging, and more time creating new products and features.
“Our AI-assisted workflows, powered by our automation-grade grouping, are an industry first to reduce the daily noise and manual work of fixing code for developers so they can focus on building and innovating,” said Rollbar CEO and co-founder Brian Rue. “Our investment in these automation features and our Continuous Code Improvement Platform – along with our company expansion, growing customer base, recent Series B funding, and new visual brand identity – highlight our commitment to help developers build software quickly and painlessly.”
Automation-grade grouping is the next-generation of Rollbar’s grouping engine. Rollbar uses machine learning to determine patterns on an ongoing basis to continuously improve the engine while also identifying unique errors in real time.
This eliminates noise caused by missed or false alarms and provides the foundation for AI-assisted workflows.
Hard-coded rules make it impossible to keep up with constantly changing error types. That’s why Rollbar regularly runs its machine learning model against new errors from customers to recognize different error types, and to group them effectively.
The three AI-assisted workflows Rollbar is introducing include:
- Rollbar Toolkit for Kubernetes, with the first workflow of the toolkit being an integration with Prometheus, Kubernetes, and Weaveworks Flagger, gives development teams the ability to automatically halt, sustain, or expand for progressive deployments using Rollbar error data. Developers no longer need to actively monitor deployments for errors and can quickly react to and resolve any critical errors.
- Automated Feature Flag Triggers, an integration with LaunchDarkly, enables development teams to proactively discover errors and instantly turn off any feature, at any time, based on new and reactivated errors associated with that feature flag. This lowers the risk of deployments and minimizes the impact on users when something goes wrong.
- Automated Issue Tracking allows users to generate tickets in any issue tracking tool (such as Jira or GitHub) automatically for any new or critical error based on predetermined rules. This helps developers save time by avoiding manual ticket generation and ensuring that all issues are being addressed.
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