---
title: "Investigate in plain language | Grafana Labs"
description: "Ask questions in plain language, generate queries and dashboards"
---

> For a curated documentation index, see [llms.txt](/llms.txt). For the complete documentation index, see [llms-full.txt](/llms-full.txt).

## Grafana Assistant

[Grafana Assistant](/docs/grafana-cloud/machine-learning/assistant/) in Grafana Cloud adds the higher-order, agentic features that need the full backend:

- [**Infrastructure memory**](/docs/grafana-cloud/machine-learning/assistant/guides/infrastructure-memory/): Assistant builds and retains context about your environment over time.
- [**Hosted Cloud MCP connections**](/docs/grafana-cloud/machine-learning/assistant/configure/cloud-mcp/): Zero-install, OAuth 2.1 MCP. Self-managed users run the local OSS MCP server with a manual service account token instead.

<!--THE END-->

- **[CLI](/docs/grafana-cloud/machine-learning/assistant/guides/cli/) auth tokens, anonymous access, and [Slack integration](/docs/grafana-cloud/machine-learning/assistant/guides/slack/)**: Cloud-native access and collaboration paths

## What keeps you up at night?

Role / WorriesWhat you get with Grafana Assistant in Grafana Cloud

**SRE, On-call Engineer**

- Getting paged with no context
- Spending 20 minutes writing queries just to understand what’s happening
- Context-switching between tools while the clock runs

<!--THE END-->

- Infrastructure memory pre-maps your services and dependencies; no time is lost rebuilding context when a page fires
- @Grafana in Slack surfaces dashboards and answers without leaving the incident channel

**Developer**

- Never sure you’re querying the right data source
- No idea what your service depends on upstream

<!--THE END-->

- Infrastructure memory already knows your environment, dependencies, and which metrics matter

**Senior Engineer, Resident Grafana Expert**

- Receiving all escalations for dashboards and queries
- Spending more time unblocking others than doing your own work

<!--THE END-->

- Skills reference your actual dashboards and metric names; team gets environment-specific guidance, not generic advice

**Engineering Manager, VP Engineering**

- Best engineers stuck on toil
- Knowledge concentrated in one or two people
- A growing stack most of the team can’t use

<!--THE END-->

- Infrastructure memory gives any on-call engineer immediate context on services and golden signals, not just seniors

**Platform Engineer, DevOps**

- Runbooks that go stale
- A gap between what the stack can do and what the team knows to do with it

<!--THE END-->

- Skills with MCP auto-approve pre-authorize tool calls to GitHub, Jira, and Slack in a remediation flow; no manual approval mid-incident

**Grafana, Platform Admin**

- Controlling who can use AI features
- Preventing Skills from triggering actions they shouldn’t
- Managing the Cloud vs self-managed feature gap

<!--THE END-->

- Assistant-specific RBAC roles control feature access at a granular level
- MCP auto-approve requires each Skill to declare pre-authorized tools; nothing runs without deliberate configuration
- Self-managed connects with one click; Cloud-only features are hidden in its UI automatically

**Security, Compliance Officer**

- AI models trained on sensitive production telemetry
- Prompts leaving your control
- No audit trail for what the AI did during an incident

<!--THE END-->

- Requests always route through Grafana Cloud, never directly to AI providers, and only minimal prompt context is sent
- Assistant operates within the user’s RBAC scope; it can’t see data the user can’t see
- Full tool use and reasoning history in every conversation gives a complete audit trail