SolarWinds Avior AI Agents Automate Network Operations, Shifting Focus for Engineers

SolarWinds has launched Avior, an agentic AI system designed to automate network operational tasks. Avior builds upon SolarWinds' existing AI framework by introducing goal-oriented agents capable of executing tasks autonomously, moving beyond mere recommendations. This initial offering includes a network engineering agent focused on automating alert management, configuration automation, compliance checks, and incident remediation. The system integrates with both SolarWinds' on-premises and SaaS products, leveraging operational data and existing management tools to resolve issues across enterprise environments. This development is significant for network practitioners as it directly addresses the persistent challenge of operational overhead and the need to scale network management without proportionally scaling human effort. By automating repetitive tasks, Avior allows network engineers to dedicate more time to strategic planning, architectural improvements, and addressing complex, non-routine issues. This shift can lead to improved job satisfaction, reduced burnout, and a more efficient allocation of skilled resources. The ability for AI agents to take action, rather than just advise, fundamentally changes the interaction model between engineers and their tools. The introduction of agentic AI in network operations aligns with the broader trend of increasing automation and intelligence in IT infrastructure. We've seen similar movements in cloud operations (AIOps), security (SecOps automation), and even software development (DevOps with AI-assisted coding). The goal across these domains is to create more resilient, self-healing, and efficient systems by embedding intelligence directly into operational workflows. This evolution is driven by the ever-increasing complexity and scale of modern IT environments, where manual intervention becomes a bottleneck and a source of errors. Huawei, for instance, has been pushing for AN L4 autonomous networks, achieving closed-loop automation for network changes and security protection in certain deployments, demonstrating the industry's collective move towards higher levels of network autonomy. In practice, network engineers should closely evaluate how such agentic AI systems can be integrated into their existing workflows. Key considerations will include the level of control and oversight provided by the system, the ability to define and enforce guardrails for autonomous actions, and the transparency of the AI's decision-making process. SolarWinds has included safeguards such as risk-based guardrails requiring human approval for potentially dangerous actions, audit trails for tracking agent activity, and automated rollback capabilities. Practitioners should start with human-in-the-loop modes for critical tasks, gradually increasing autonomy as trust and understanding of the system grow. The focus should be on leveraging these tools to augment human capabilities, not replace them entirely, thereby enabling networks that are not only smarter but also more secure and reliable.