Who checks the Grafana assistant's investigations?

The source does not describe a review process. The proposed design, not Grafana's, has the agent investigate while a site reliability engineer checks the diagnosis and decides remediation, with read-only exploration kept separate from dashboard editing and production changes.

How strong is the evidence for the Grafana example?

The evidence is early-stage. The cited page describes an implemented private preview, and broad general availability is not established. Preview cohort size, production incident accuracy, grader methodology and reset support are not disclosed.

What would an enterprise need to build something similar?

This is Nuvepro's proposed design, not Grafana's. It would need a licensed test tenant with instrumented services, versioned metric, log and trace fixtures, and known injected incidents. Checks would cover query execution, dashboard validity, root-cause agreement and engineer review of telemetry gaps.

Limited published evidence · Observability and IT operations

AI service investigation dashboards: Grafana Labs example

Grafana Labs built an assistant on Anthropic's Claude Platform that finds metrics, writes PromQL and LogQL queries, builds dashboard panels and investigates logs and latency inside Grafana's interface. The source describes a private preview, not broad production adoption.

Service investigation and dashboard creation across telemetry tools

Grafana Labs

Published account: Implemented private preview; broad general-availability/production adoption is not established by the cited page.

A published third-party example, not a Nuvepro customer case or evidence of a partnership.

The workflow at a glance

Grafana Labs reported work

Illustrative summary of the published account

Reported work components. Their arrangement does not imply a fixed execution order or simultaneous work.

Interface
Grafana assistant
Works within Grafana observability context.
Telemetry work
Queries and dashboard panels
Finds metrics, constructs PromQL/LogQL and investigates latency or logs.

Published account: Implemented private preview; broad general-availability/production adoption is not established by the cited page. Preview cohort size, production incident accuracy, grader methodology and reset support are not disclosed. See the source account and limitations below.

What the source reports

The assistant finds metrics, constructs PromQL and LogQL queries, builds dashboard panels and investigates logs and latency. It exposes tool use across multiple steps. The published implementation lives within Grafana's own interface, giving users access to their existing observability context rather than a separately reproduced enterprise.

Reported technology: Anthropic Claude Platform. Scope and outcomes are source-reported.

What the enterprise environment would need

The following is a proposed project design for an enterprise doing similar work. Published details are marked; the remaining systems, data and scenarios would be agreed for your project.

Proposed practice environment

Investigate a service incident from telemetry

Illustrative project design to scope with your enterprise.

1 · Set up the environment
Instrumented incident
Frozen metrics, logs, traces and dashboard state accompany a seeded service fault.
2 · Preparation and agent contributions
Investigate and draft panels
Run approved test queries and prepare a diagnosis with telemetry references.
3 · Independent checks
Check against known fault
Execute queries, validate panels and compare incident timing with independent fault evidence.
4 · People decide acceptance
SRE reviews diagnosis
Accept supported findings and unresolved telemetry gaps for the bounded incident.

Only within the accepted practice scope

Practice output

A reviewable evidence package

A test dashboard and evidence-linked investigation report.

If a check fails → withhold acceptance.

A misleading correlation or missing telemetry produces an unsupported cause. Withhold acceptance, repair, independently recheck and return to owner review.

Failure or owner rejection returns to review

Hold
→ 2 · Revise work
→ 3 · Rerun independent checks
→ 4 · Owner review

Operating boundary. Read-only telemetry and test dashboards; no production remediation.

Proposed, not an executed Nuvepro project or a description of the named company’s implementation. Owners agree the data, rules, responsibilities and acceptance criteria for their enterprise.

Systems and tools: Published: Grafana telemetry tools. Proposed: use a licensed test tenant with instrumented services and real query engines.
Data and state: Proposed: version metric, log and trace fixtures plus dashboard state; synthetic text alone cannot establish query behavior.
People and handoffs: Proposed: agent investigates; SRE checks diagnosis and decides remediation.
Rules and authority: Proposed: separate read-only exploration, dashboard editing and production remediation permissions.
Normal and exception cases: Proposed: inject known service incidents, missing telemetry and misleading correlations.
Reset and replay: Proposed: restore telemetry windows and dashboards; replay the same incident against changed agents. Not published in this case.

What needs checking

Published case gives no independently quantified investigation accuracy.
Proposed: query execution, dashboard validity and root-cause agreement against instrumented fault injection.
Proposed: SRE review of topology, telemetry gaps and incident timing; compare with known real incidents.

Review environment fidelity, human capability and agent performance separately. The enterprise’s accountable owners decide acceptance of the shared workflow and any operating use.

Questions about this example

What does Grafana Labs use AI for?: Grafana Labs built an assistant on the Anthropic Claude Platform. According to the source, it finds metrics, constructs PromQL and LogQL queries, builds dashboard panels and investigates logs and latency, using multiple tool steps inside Grafana's own interface.
Who checks the Grafana assistant's investigations?: The source does not describe a review process. The proposed design, not Grafana's, has the agent investigate while a site reliability engineer checks the diagnosis and decides remediation, with read-only exploration kept separate from dashboard editing and production changes.
How strong is the evidence for the Grafana example?: The evidence is early-stage. The cited page describes an implemented private preview, and broad general availability is not established. Preview cohort size, production incident accuracy, grader methodology and reset support are not disclosed.
What would an enterprise need to build something similar?: This is Nuvepro's proposed design, not Grafana's. It would need a licensed test tenant with instrumented services, versioned metric, log and trace fixtures, and known injected incidents. Checks would cover query execution, dashboard validity, root-cause agreement and engineer review of telemetry gaps.

Shape this around your enterprise

Bring a similar workflow and the people responsible for it. Together, define the records and systems to use, the result your team will build or configure, and how its owners will check it. Nuvepro scopes the simulation, environment and preparation. Agree any existing-agent evaluation, implementation assistance and production responsibilities separately.

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