Causely today announced it has been named a Gartner® Cool Vendor for AI in IT Operations.¹ Causely helps engineering and operations teams continuously assure performance for business-critical, cloud-native applications at scale. The recognition reflects a growing shift in engineering toward AI that continuously analyzes telemetry using distributed processing and causal graphs to infer the underlying drivers of anomalous symptoms.

Business-critical applications are often comprised of dozens of services, shared infrastructure, and third-party services. Every release, configuration change, traffic shift, and capacity adjustment can degrade system behavior, and small regressions can translate directly into lost revenue, reduced customer trust, and degraded engineering productivity. Observability dashboards surface what happened, and reactive AI agents can accelerate ad hoc triage by piecing together signals after symptoms appear. But for business-critical applications where performance must be assured to avoid revenue loss, that approach is insufficient. Teams need a reliability operating system that runs continuously alongside the infrastructure and infers the causes of observed degradations as they emerge.

“We’re honored to be recognized by Gartner for our innovation,” said Yotam Yemini, CEO of Causely. “When your business depends on digital experiences, ad hoc triage and reactive agents will fall short. You need a system that runs continuously, maintains a live causality graph, and guides safe automated action.”

Causely deploys locally as a lightweight, containerized system and continuously maintains a structured causality graph of causes and observations. Using distributed local processing, Causely analyzes live telemetry to detect anomalies and infer their underlying causes. Because telemetry is processed locally, engineers do not need to rely on sampling, worry about central pipeline scalability, or absorb the cost and security implications of exporting raw production data. Causely provides clear direction on when to act, where to focus, and what intervention is most likely to reduce performance risk. Through a comprehensive set of APIs, webhooks, and an MCP server, teams can connect this inference directly to workflows and automated action.

The result is continuous performance assurance for the applications that matter most. Teams detect risk earlier, align quickly on what is actually driving impact, and take safer action with greater confidence. Causely helps teams prevent issues before they happen by turning inferred drivers into engineering priorities, architectural improvements, and automated guardrails, even when the specific failure mode has not been seen before.

Causely complements observability platforms by providing an always-on reasoning layer that integrates into existing workflows through APIs and automation. It is built for organizations running complex, high-change systems where reliability is a business requirement, not just an operational metric.

About Causely

Causely is the reliability operating system for business-critical, cloud-native applications. Causely continuously analyzes telemetry to infer what is driving performance degradation and connects those insights to operational workflows and automation. Deployed locally, Causely helps teams assure performance at scale without the cost and security implications of exporting raw production data.

¹ Gartner, Cool Vendors for AI in IT Operations. Cameron Haight, Hassan Ennaciri, Chris Saunderson. 02 December 2025. (Report available to Gartner clients: https://www.gartner.com/en/documents/7233330)

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The recognition reflects a growing shift in engineering toward AI that continuously analyzes telemetry using distributed processing and causal graphs to infer the underlying drivers of anomalous symptoms.