Virtana launches agentic SLA management for AI ops

Virtana has launched Agentic SLA Management on its observability platform, a new feature designed for hybrid infrastructure, multi-cloud environments and AI systems.

The product lets organisations define service-level agreements as code, monitor service performance against business commitments, and automate alerting, response, remediation and optimisation workflows.

Virtana is positioning the release as a response to growing operational strain in enterprise IT as companies add AI workloads to already complex estates spanning on-premises systems, cloud services, Kubernetes, networks, storage and databases.

Its research found that 75% of enterprises report double-digit AI job failure rates, which it says reflects the limits of manual operations based on dashboards, tickets and reactive workflows.

How It Works

According to Virtana, the new function is built on its MCP Server and full-stack observability platform. It uses operational data from applications, services, infrastructure, cloud platforms and AI workloads to assess service performance and trigger actions when risks emerge.

Organisations can use natural language through the MCP Server to create, manage and automate service assurance workflows. The system is also intended to govern service objectives, operational thresholds and business commitments through a single framework.

Virtana has grouped the automation around four software agents focused on alerting, incident response, remediation and performance optimisation.

The Alert Agent is designed to identify and prioritise telemetry across the technology stack. The Response Agent evaluates business impact and coordinates response, communications and escalation workflows.

The Remediation Agent is intended to identify root causes and either execute or recommend corrective actions. The Optimisation Agent analyses service performance, infrastructure use, workload efficiency and operational patterns.

Operational Pressure

The launch comes as infrastructure teams face pressure to support AI applications without undermining service levels in other parts of the business. Virtana argues that fragmented monitoring tools often show symptoms rather than underlying causes, making it harder to link technical issues to business impact.

Paul Appleby, Chief Executive Officer at Virtana, linked the product launch to that broader shift in enterprise operations.

“Human-managed operations has reached its limits,” said Paul Appleby, Chief Executive Officer at Virtana.

“AI, hybrid infrastructure and distributed systems have created a level of complexity that can no longer be managed through dashboards, tickets and manual workflows. The next generation of enterprise operations will be defined by autonomous systems that understand service commitments and business priorities, assess risk continuously, and act before outcomes are impacted,” Appleby said.

Virtana said the system aims to move service-level management away from retrospective reporting and towards continuous control. That would mean linking service commitments directly to automated operational decisions rather than checking compliance after incidents occur.

The company also argued that infrastructure bottlenecks are becoming a business issue as AI adoption grows. In the same research, 56% of practitioners cited storage and networking bottlenecks as their top AI constraint.

Appleby said that trend raises the stakes for companies trying to operate AI systems at scale.

“Organisations have spent decades measuring service levels. The next decade will be defined by assuring them,” said Appleby.

“As AI increases the scale and complexity of enterprise operations, agentic systems are only as effective as the operational context they can access. Organisations that can connect service commitments, operational risk and business outcomes into a unified system will be positioned to operate AI at scale with greater reliability, efficiency and control. Our research found that 56% of practitioners cite storage and networking bottlenecks as their top AI constraint, highlighting how infrastructure limitations increasingly translate into business risk,” Appleby said.