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Use case 04 · QA

QA is blocked on unclear requirements

QA loses time reconstructing intent. Testing can start from the wrong assumption and clarification cycles cost a day or more.

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The work already crosses these systems:

JiraConfluenceGitHubNopsAI run

Before and after

From scattered checks to one governed run.

Today

A tester reads the ticket, hunts for the linked spec, skims the pull request, then waits for the product manager to answer in a thread.

With NopsAI

A QA-readiness pipeline pulls the ticket, linked spec and PR context, then an AI goal step drafts acceptance criteria, mismatches and questions for the PM.

Ambiguous requirements become a structured, reviewable brief before testing goes sideways.

Governed run

The run, step by step.

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

Knowledge contextInclude and ignore rulesAI goal stepsGitHub read-only profileOutput sharing control

  1. Trigger

    A tester starts the run manually from the ticket, or a label change fires it automatically.

  2. Collect context

    Ticket, linked specification and pull-request diff are read through a read-only GitHub profile.

  3. Verify state

    Deterministic checks confirm the PR targets the expected branch and the spec version is current.

  4. Reason

    An AI goal drafts acceptance criteria and lists every mismatch between spec, ticket and code.

  5. Approve

    The product manager reviews the questions before they reach the team channel.

  6. Execute and record

    The brief is attached to the run and shared with sharing controls intact.

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.

← Production incident at 2 a.m.

At 2 a.m. the hard part is finding what changed and deciding whether rollback is safe — fast, without losing the evidence trail.

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Preparing a release →

Scope mismatch, unmerged PRs, failed gates and an outdated runbook can all cause a late surprise.

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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.

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