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Platform overviewWorkflow orchestrationGitOps configurationGovernance and AAAAI and MCPRuntime and executionEvidence and monitoring

Use cases

All use casesProduction incidentRelease preparationHotfix to productionSecurity scan triage
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Use case 01 · Support to release

A customer-reported bug needs to ship as a hotfix

The fix is small, but no single durable trail connects the complaint, the code change, the test result and the emergency approval.

The work already crosses these systems:

SalesforceJiraConfluenceGitHubCI/CDServiceNowNopsAI run

Before and after

From scattered checks to one governed run.

Today

Support copies the case into Jira, an engineer hunts for the spec in Confluence, patches the code, chases CI, then files a ServiceNow emergency change from memory.

With NopsAI

One governed run gathers case, ticket and spec context through approved tools, runs fix, test and build in an isolated container, pauses for approval, and attaches full run evidence to the emergency change.

It does not just automate the fix. It makes the whole emergency path repeatable and auditable.

Governed run

The run, step by step.

Deterministic work first, reasoning inside a boundary, and a named human before anything high-risk executes.

MCP profiles per stepKnowledge contextCI/CD executionApproval gateIsolated runtimeAudit trail
  1. Trigger

    A support escalation or a labelled Jira issue starts the run through an approved webhook source.

  2. Collect context

    Case history, ticket detail and the linked specification are read through read-only MCP profiles.

  3. Verify state

    Deterministic steps confirm the affected version, branch protection state and current release train.

  4. Reason

    An agent role summarizes the defect, the proposed patch surface and the regression risk.

  5. Approve

    A release owner reviews the proposal; self-approval is disabled for the production scope.

  6. Execute and record

    Build and test run in an isolated container; the change record links back to the run evidence.

Evidence

What the run leaves behind.

The useful part is not only the automation. It is repeatability with proof.

Trigger and subject

What started the run and which effective identity it ran as.

Authorization snapshot

Which resources were checked, and which decision each check returned.

Tool and AI activity

Every tool call, the profile that allowed it, and the model usage it consumed.

Approvals and outputs

Who approved, when, and the deliverables the run produced.

Pipeline runs overview showing status, run identifiers, durations and outputs.

Map this workflow against your controls.

Bring the trigger, the tools it touches, the approvers, the runtime boundary and the evidence you need to keep.