Xurrent Introduces Autonomous AI Agents and Open MCP Server for IT Operations

May 12, 2026
Xurrent has launched autonomous AI agents and an open Model Context Protocol server, enabling IT teams to delegate routine tasks and connect external AI models within a unified governance framework.

Xurrent announced in a press release the introduction of autonomous AI Agents and an open Model Context Protocol (MCP) server for its service and operations management platform. The new agents perform IT service tasks such as triage, documentation, and ticket closure, while the MCP server connects the platform to external AI models from providers like OpenAI, Google, or Anthropic.

The AI Agents act as digital members of IT teams with defined roles and human oversight. Their actions are logged and governed under the same policies as human users. This design allows teams to assign repeatable tasks to agents while retaining full visibility and control over operations.

The open MCP server lets customers integrate any external AI model into Xurrent. These external agents adopt the same governance, security, and audit frameworks as native agents, ensuring consistent behavior across systems. This unified structure positions Xurrent as a central layer where IT operations and enterprise AI converge.

According to Xurrent, AI capabilities are included across all license tiers, removing usage-based pricing. The company stated that the platform was built from the start for integrated AI operations rather than adding AI as an optional feature.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Enterprise AI Brief, AI Programming Weekly or Daily AI Brief.

Also, consider following us on social media:

Free newsletter

Enterprise AI Brief

Weekly report on AI business applications, enterprise software releases, automation tools, and industry implementations.

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more
Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.