Grafana Labs Adds AI Watchers and Code-Fixing Agents to Its Observability Platform

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Grafana Labs is expanding Grafana Cloud with AI agents that watch for emerging problems, tools that prepare code fixes for human review, and a shared knowledge graph connecting infrastructure failures to the users they affect. Announced at ObservabilityCON 2026, the releases address two increasingly connected jobs: using AI to operate software and monitoring the AI systems entering production.

The rollout combines generally available capabilities with public and private previews. Assistant Watchers and Agent Observability with OpenTelemetry support are generally available, while Grafana Cloud Agents enters public preview. Alongside them, Grafana is introducing faster log searches, additional telemetry cost controls, and a commercial plugin marketplace.

The underlying question is how far an operations team should let an agent go. Finding an unusual pattern, explaining a failure, and changing production code carry different consequences. Grafana’s announcement brings those steps closer together while retaining human approval for proposed code fixes.

Assistant Watchers lets teams describe what they want monitored in ordinary language. Rather than relying exclusively on a predefined threshold, a Watcher learns patterns from telemetry and surfaces evidence when those patterns change. Findings route through existing Grafana Alerting, and a critical finding can initiate a deeper investigation.

That extends Grafana Assistant, the company’s built-in agent for asking questions about operational data. Assistant Investigations, generally available since July, can perform longer investigations initiated by alerts or incidents. The new Watchers supply another starting point: an emerging change that a team may not yet have encoded as an alert rule.

Grafana Cloud Agents adds a controlled cloud environment where Assistant can examine repository code alongside live telemetry, prepare a proposed fix, and open a pull request. The public preview stops at a reviewable change for a person to approve. A plausible diagnosis and a working patch still need validation against the application’s requirements and deployment process.

This matters because an agent can produce a convincing explanation without having enough evidence. Connecting each finding to telemetry gives engineers something to inspect, while a pull request creates a familiar checkpoint for reviewing the actual change.

The other half of the announcement concerns AI workloads already running inside applications.