meshIQ adds agentic AI & petabyte telemetry support

meshIQ has launched Version 12.1 of its middleware management platform, adding agentic AI features and support for petabyte-scale telemetry analysis.

The update targets enterprises managing large messaging, event-processing and streaming environments that are increasingly difficult to monitor through separate tools and fragmented operational data.

According to meshIQ, Version 12.1 brings management, observability and tracking into a single platform. It also introduces a browser-based Command Centre that gives operators one operational view across middleware systems without relying on multiple interfaces.

The release is aimed at middleware administrators, platform engineers and other operations staff managing brokers, queues, topics, routes and transaction paths across hybrid, mixed-technology estates.

Data scale

A central part of the update is support for very large telemetry volumes. As enterprises move from gigabyte-scale monitoring to petabyte-scale operational data, the need extends beyond collection to ingesting, indexing and analysing that information in ways that help teams distinguish routine changes from anomalies that need attention.

This data layer is intended to support more predictive operations in middleware environments, where teams often face manual investigation and slow troubleshooting when incidents emerge across complex message flows.

Version 12.1 also expands platform management across technologies including Apache ActiveMQ, Apache Artemis, Apache Kafka, RabbitMQ and IBM MQ, along with other messaging and streaming platforms.

Other additions include guided onboarding and configuration workflows, broader governance controls and operational safeguards designed to improve auditability and provide more context around system changes, access and high-impact administrative actions.

meshIQ positioned the release as part of a wider shift in enterprise infrastructure operations, as integration layers carrying messages, events and transactions become more critical when companies connect legacy messaging systems with newer streaming platforms.

Greg DeaKyne, Vice President of Product Management at meshIQ, said the challenge for large organisations is making sense of data in context rather than simply collecting more of it. "At enterprise scale, the hardest part of middleware operations is not just seeing more data; it is understanding what that data means in context," he said.

"Version 12.1 was built to give operators a clearer view of what is happening across brokers, queues, topics, routes, and message flows, so they can identify patterns faster, reduce log-diving, and fully harness predictive intelligence and agentic AI in real operating settings," DeaKyne said.

Operational focus

meshIQ said the Command Centre is intended to simplify technology onboarding and navigation across middleware activity. That reflects a broader effort to reduce dependence on specialist knowledge tied to individual vendors or products, especially in estates that combine open-source and established enterprise systems.

The company also highlighted support for Apache ActiveMQ, saying enterprises adopting open-source middleware need management tools and expertise to reduce operational risk as those systems move into production environments.

The broader aim is to shift organisations from reactive troubleshooting to earlier issue detection and faster analysis. In practice, that means using telemetry from middleware components to identify patterns before they become service-level failures or broader business disruption.

Navdeep Sidhu, Chief Executive Officer of meshIQ, said the integration layer has become a growing weak point for enterprises operating at scale. "The integration layer carries the messages, events, and transactions that power revenue, supply chains, and customer experiences, but it's breaking at scale," he said.

"With Version 12.1, we are helping enterprise leaders modernize that critical infrastructure while operating more leanly and intelligently. By combining unified middleware management, petabyte-scale intelligence, and the foundation for agentic AI, organizations can reduce risk, improve efficiency, and build the flow intelligence needed to keep critical business processes moving," Sidhu said.