Observability software pioneer Grafana Labs Inc. is flexing its muscles in the fast-growing market for artificial intelligence monitoring tools, unveiling a series of updates to its Grafana Cloud platform today at ObservabilityCON 2026.
The startup announced a new suite of AI agents, including Assistant Watchers to monitor AI workloads and autonomous agents, and Grafana Cloud Agents to troubleshoot issues, in addition to various broader enhancements focused on monitoring and understanding AI telemetry data.
Grafana initially made a name for itself in the traditional observability space with its open source platform Grafana and has been aggressively focusing on AI observability since the turn of the year. The original Grafana stack was designed to monitor the cloud infrastructure and software applications of the day, and the startup was one of the first to realize that generative AI applications behave very differently.
Because traditional apps work so differently, legacy observability tools struggled to provide comprehensive insights into AI models and agents, making debugging difficult. Grafana’s solution is to treat agent sessions and conversations using rich language models as standard telemetry signals, similar to the logs, metrics and traces associated with traditional applications. This allows customers to monitor the performance of these systems in the context of their broader information technology infrastructure.
Grafana is responding to the emergence of autonomous agents performing increasingly complex tasks on behalf of humans by introducing highly specialized AI agents specifically designed to observe the behavior of these systems. The company said it is pushing into “agent operations,” and a key part of that push is its new Assistant Watchers, which are always-on agents designed to observe and learn other agents’ normal telemetry patterns and detect anomalies in their behavior before major problems arise.
In addition to Assistant Watchers, the preview also introduces Grafana Cloud Agents, which provide a controlled environment in which AI can analyze code in the context of its production behavior and prepare software fixes that can be reviewed by humans. Meanwhile, Pages is a new feature that enables teams to create custom, app-like visualizations of AI workloads that provide deeper insights than standard dashboards.
Mat Ryer, senior director of AI at Grafana, said companies want AI agents to take over more work from humans, but if they want to do that, they need both evidence and control at every step. “Observers uncover emerging problems, investigations help diagnose them, and cloud agents prepare fixes for human review,” he said. “Together, they make Grafana Cloud more autonomous and unify detection, diagnosis and suggested actions in a workflow based on the team’s own telemetry.”
Grafana’s agents may be quite sophisticated, but the company recognizes that observability needs to do more than just secure AI workloads. One problem that needs to be solved is the cost of running AI in the cloud. To this end, query performance improvements are being introduced in Grafana Cloud Logs, based on the technology the company gained through its recent acquisition of Logline. This enables faster full-text searches over low-frequency data, such as: B. specific request IDs, at no additional cost, according to Grafana.
Another update is the new Adaptive Logs Archive within Grafana’s Adaptive Telemetry Suite, which enables enterprise users to forward logs to their own object storage buckets using open data formats, allowing them to retain observability data for future investigations and compliance audits at a lower cost.
Finally, Grafana said that with the launch of its new Knowledge Graph and Digital Experience Monitoring capabilities, the company is seeking to achieve full stack visibility for both AI and traditional cloud workloads. Knowledge Graph can connect infrastructure and application dependencies with real-world user experiences, allowing both AI agents and human engineers to see a common runtime context when investigating problems. This capability is expanded with new Mobile Observability and Session Replay tools that enable teams to view issues from the user’s perspective.
“Most teams don’t have a monitoring problem,” said product director Richard Lam. “What they have is a technology debt problem: telemetry from every layer and no way to connect it to the customer it affects. Putting a knowledge graph into every Grafana Cloud stack and extending it from the infrastructure to the user makes unified observability the foundation, rather than something each team has to put together themselves.”
Today’s announcements show that Grafana has a clear vision for the rapidly confusing future where AI is used for observability and observability is a must for AI.
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