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How SolarWinds Used An AI Agent To 5x Its Software Engineering Output
TK

Tim Keary, Contributor

· 1 min read

World NewsForbes: Innovation

How SolarWinds Used An AI Agent To 5x Its Software Engineering Output

Software engineering is being redefined by AI agents. Since March, an internal team at SolarWinds known as “Area 51,” has been building an AI agent designed to automate IT operations. The organization claims the agent has increased engineering output fivefold compared to human engineering teams at the company.

The project contributed to the release of Avior, an agentic AI system designed to automate remediation across IT environments which SolarWinds announced today. Avior has tasks encoded into the agent as goals or outcomes that IT teams can use to coordinate specialist agents across tasks including correlating related alerts, continuously auditing devices, remediating configuration drift, implementing configuration backups, tracking capacity and managing the asset lifecycle.

According to SolarWinds lead developer, Peter Choi, Area 51 began as a team of four engineers located across the U.S who collaborated to build an agent over Zoom and Teams calls, and has since scaled to approximately 25 employees. The objective was to create an agent that could reduce customers “daily toil,” across IT operations, which the team attempted to achieve by building an agent that could automatically remediate these issues.

With the help of AI coding tools, SolarWinds developed a prototype agent within approximately two weeks, which was requested by a dozen trial customers within just two months of its development. The project highlights how AI agents are transforming development and IT operations across the enterprise.

AI Agents As The New Bottleneck

Many organizations are now looking to AI agents to streamline IT operations. For example, technology advisory firm The Futurum Group, estimates that 49.2% of organizations are planning agentic AI deployments in IT operations and cybersecurity.

“We want to shift the conversation from how intelligent a model is to how much useful work can we reliably trust agents to complete in these complex environments,” Sai told me in a video interview.

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This story was published by Forbes: Innovation and written by Tim Keary, Contributor. SyncAI.news shows a preview; the complete article is on the publisher's site.

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