Fill in incident custom fields without leaving Slack
UpdateyesterdayUnverifiedAdded Oct 14, 2026
Incident custom fields in Grafana IRM work in the Grafana Cloud app for Slack. Responders can record the details your team tracks without switching to the browser. Declare an incident The Declare incident dialog box includes your organization’s mandatory custom fields. Fill them in when you declare the incident, or leave them for later.
Topics: Observability
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Every Grafana Cloud releaseFilter and sort alert groups by date in Grafana IRM
You can now filter and sort alert groups in Grafana IRM by when they were created, last received an alert, acknowledged, or resolved. Want to review what your team resolved last week? Select Resolved At from the drop-down next to the time range picker, then select Last week . To narrow your results, combine date filters with Add filter .
Silences in Grafana IRM now last as long as you set them
When you silence an alert group in Grafana IRM, the silence now lasts for the full duration you choose, even if the source resolves and fires again. Previously, if the source sent a resolve signal while an alert group was silenced, the alert group resolved immediately and the silence ended with it.
New escalation options in Grafana IRM
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Custom headers and footers for report PDFs
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Connect Synthetic Monitoring checks to your services with Knowledge Graph
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Synthetic Monitoring folders are now Generally Available
In June 2026, we announced the public preview release of folders in Synthetic Monitoring . With folders, you can group checks by team, service, or environment so the right checks are always easy to find and act on a whole group at once instead of one check at a time. Folders in Synthetic Monitoring are now generally available.
Plan your dashboard before building it with Grafana Assistant
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Role managers can revoke access they do not hold
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Also shipped on Oct 13, 2026
Grafana Labs in October 2026What's included:
- Three training methods: Supervised fine-tuning (SFT) , direct preference optimization (DPO) , and reinforcement learning with group relative policy optimization (GRPO) . Each method trains a low-rank adapter (LoRA) or the full model weights. Custom loss functions : Train with any objective you compute in PyTorch from the model's log-probabilities.
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