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Use case 02 · Planning

Sprint planning for the next two weeks

Planning starts with manual research instead of decisions. Blockers surface late and half-ready stories enter the sprint.

The work already crosses these systems:

JiraConfluenceMonday.comNopsAI run

Before and after

From scattered checks to one governed run.

Today

The scrum master checks Jira backlog and velocity, reads Confluence for open design questions, and cross-checks Monday.com for dependencies before the meeting even starts.

With NopsAI

A scheduled pipeline pulls backlog, velocity, spec-readiness and dependency data, uses a fast LLM profile to summarize story readiness, and drops a planning brief automatically.

Planning starts with prepared evidence instead of tool-hopping.

Governed run

The run, step by step.

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

Scheduled triggersMCP and API integrationsFast LLM profileScoped variablesOutput generation
  1. Trigger

    A schedule fires the morning before planning, using a service account scoped to the delivery team.

  2. Collect context

    Backlog, velocity history, spec pages and cross-team dependencies are read through approved tools.

  3. Verify state

    Deterministic checks flag stories missing estimates, acceptance criteria or an owner.

  4. Reason

    A fast LLM profile summarizes readiness per story and highlights the dependencies that will bite.

  5. Approve

    No gate needed — the run produces a document, not a change.

  6. Execute and record

    The planning brief is published as a run-owned Markdown output the team can cite in the meeting.

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.