Grafana is trying to close the AI ​​visualization gap before business agents take over

Startup company Grafana Labs Inc. said today that it is trying to illuminate the “black box” in the inner workings of artificial intelligence models by introducing new capabilities that will better enable companies to trust and control them in production.

The announcement came during Grafana’s annual GrafanaCON 2026 user conference in Barcelona, ​​where it also revealed it is creating a dedicated “AI organization” to be led by its new AI Director Mat Ryer.

Grafana said that many businesses are rushing to integrate AI agents into their operations with the aim of improving automation, but doing so brings many challenges in terms of visibility. First and foremost is that AI applications behave very differently compared to traditional software which helps Grafana establish itself as a major virtual player.

Because they work so differently, existing monitoring tools struggle to provide too much insight into agents, making them difficult to remove. In addition, developers often find themselves constantly switching between coding environments like GitHub Copilot and Cursor and the visualization tools they use to keep tabs on their code in production.

The implementation solution is to handle agent sessions and large language model conversations as standard telemetry signals, such as logs, metrics and traces associated with common applications. The idea is that users will be able to monitor the performance of agents in the context of their wider information technology infrastructure.

The biggest new update is the new AI visualization capabilities that launched as a public preview in Grafana Cloud today. It is tasked with observing the behavior of AI agents, including their input, output and execution flow, so that this can be continuously monitored for low-quality responses, policy violations and other ambiguous activity. According to Grafana, it is able to reveal vulnerabilities such as data exposure or leaked information much faster than existing monitoring tools not created for AI agents.

Developers may be excited about the new Grafana Cloud CLI, or GCX, command-line tool, which is a new “agent interface” designed to sit on the desktop. So rather than having to jump from their integrated development environment to the Grafana dashboard, they can use GCX to invoke the Grafana helper directly from places like Claude Code or GitHub Copilot. The idea is to create a “continuous feedback loop,” where developers can constantly monitor their AI agents in production, incorporate alerts and receive suggested code fixes in real time.

Behind the scenes, Grafana says, it’s tweaking its core engine to better deal with the big data generated by AI systems. The main focus here is Grafana’s log aggregation tool, updated in Grafana Loki Evolution. Loki has been rebuilt from the ground up on an Apache Kafka-based architecture and comes with a new query scheduling tool that speeds up ten times the performance of clustered queries while scanning 20 times less data.

Grafana is also making its AI-powered Grafana Assistant tool available to more users. Previously limited to Grafana Cloud only, the AI ​​sidekick is now integrated with on-premises Grafana Enterprise deployments to cater to customers with strict data privacy requirements. It also gains new features such as an “assistant workspace” for full-screen interaction and an “assistant API” that can export Grafana’s data to third-party tools.

Finally, in a nod to the open source community, Grafana has published a new benchmarking tool called o11y-bench. It was designed to measure how well AI agents perform various real-world tasks, such as fixing a broken app dashboard or investigating an outage, against a live Grafana stack. According to the company, it is intended to be a standard “IQ test” that measures the performance of virtual AI agents.

Although not stated, today’s updates reflect Grafana’s long-term vision. If AI agents are going to start running businesses at scale, they’re going to need a manager. By creating a dedicated AI unit, tasked with “coordinating work across AI visualizations, assistant experiences and agent-driven workflows,” Grafana is poised to take on that role before anyone else steps up.

Image: SiliconANGLE/Microsoft Designer