DEVOPS

Quarterly DORA Narrative Review for Leadership

Once a quarter, an agent pulls all four DORA metrics from Snowflake, writes a plain-English performance narrative with wins and risks.

CategoryDevOps
Enginepaperclip
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFirst-business-day-of-quarter schedule
  • ActionQuery quarter and prior-quarter DORA metricsSnowflakeSnowflake
  • ActionAgent drafts interpreted performance narrative
  • LogicValidate required narrative sections present
  • OutputPublish review page to ConfluenceConfluenceConfluence

What it does

This workflow produces the quarterly engineering-performance writeup that someone usually assembles by hand. An agent reads the quarter's four DORA metrics from Snowflake, compares them to the prior quarter and to DORA performance bands, and drafts a narrative that explains what moved, why it likely moved, and what the risks are. It then publishes the writeup as a Confluence page ready for the leadership review.

When to use it

Use it ahead of quarterly business reviews or board updates when you need an interpreted story, not just a chart. Best for engineering leaders who want consistent, defensible narratives without spending a day writing them.

How it works

  1. 1A schedule fires on the first business day of each quarter.
  2. 2The flow queries Snowflake for deploy frequency, lead time, change-failure rate, and time-to-restore across the quarter and the prior one.
  3. 3The agent reasons over the deltas, maps each metric to its DORA performance band, and drafts a structured narrative with highlights and risks.
  4. 4A logic step checks the draft for required sections before publishing.
  5. 5The final step creates a formatted Confluence page under the engineering space.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SnowflakeWarehouses, queries, shares.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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