Coding Workflows Workflow Intermediate

AI Deployment & Release Workflow

Cross the gap between 'tests pass' and 'safe in production' — assess release readiness, plan the deploy and its rollback, and set up the monitoring and launch checks before you ship, not after.

The problem

'The tests pass' and 'it's safe in production' are not the same statement, and the gap between them is where launches go wrong. Tested code still has to ship: someone has to judge whether it's actually ready, plan how it rolls out and how it rolls back, decide what to watch, and confirm the launch is healthy instead of hoping. Skip that and a green build becomes a 2am incident. This workflow owns the tested-to-shipped gap — release readiness, deployment and rollback planning, monitoring, and launch validation — so shipping is a deliberate step, not a leap.

Recommended workflow

Each step uses an existing NewPrompt tool, pre-filled by a matching resource. Open the resource to read it, or jump straight into the tool with the inputs ready.

  1. Put the model in a release engineer's seat

    Anchor the model to think like someone shipping to production — reasoning about deploys, rollbacks, and what breaks at launch — instead of someone writing the next feature. The mindset is risk and recovery, not code.

    Goal The model reasoning about release risk, rollout, and recovery.

    Resource DevOps Engineer Role Prompt
    Tool Role Prompt Generator
    Open this step in Role Prompt Generator

  2. Run a release-readiness review

    Name the gaps between 'tests pass' and 'safe to ship': config and secrets, migrations, feature flags, dependencies, and the checks a green build doesn't cover. Readiness is a decision, and this is where you make it honestly.

    Goal An honest readiness assessment — what's truly ready and what blocks the ship.

    Resource Production Readiness Review Prompt
    Tool Code Review Prompt Generator
    Open this step in Code Review Prompt Generator

  3. Plan the deploy and the rollback

    Lay out how the change reaches production — the sequence, the migration order, the cutover — and, for each step, how you undo it. A deploy plan without a rollback plan is a bet, not a release.

    Goal A deploy sequence with a rollback path for every step.

    Resource Rollback Plan Prompt
    Tool Multi-Step Prompt Builder
    Open this step in Multi-Step Prompt Builder

  4. Set up monitoring and launch validation

    Decide what to watch and how you'll know the launch is healthy — the signals, the thresholds, the checks that confirm success and the ones that trigger a rollback — and write it into a runbook before you ship.

    Goal A launch runbook: what to watch, how to confirm health, when to roll back.

    Resource Monitoring & Alerting Plan Prompt
    Tool Markdown Output Builder
    Open this step in Markdown Output Builder

Expected outcome

A release you can ship with your eyes open — readiness assessed honestly, a deploy plan with a rollback for every step, and monitoring plus launch validation written into a runbook — so tested code crosses into production deliberately instead of becoming the next incident.

Best for

  • Taking tested software to a safe production release
  • Planning a deploy with a real rollback path
  • Defining monitoring and launch validation before shipping

Not for

  • Responding to an outage that's already happening — use the AI Production Incident Workflow
  • Reviewing exposed paths for vulnerabilities — use the AI Security Review Workflow
  • Building the test suite itself — use the AI Test Generation Workflow

FAQ

How is this different from the AI Production Incident Workflow?

This is what you do before and during a release to keep production safe; the incident workflow is what you do after something has already broken in production. One prevents the 2am page, the other handles it. Run this to ship; run that when a ship goes wrong.

Isn't 'tests pass' enough to ship?

No — and that's the whole point of this workflow. Tests prove the code does what you asked; they say nothing about config, migrations, rollout order, rollback, or whether you'll notice a bad launch. This owns exactly that gap between tested and shipped.

Does this deploy my software for me?

No. It produces the readiness call, the deploy-and-rollback plan, and the launch runbook — the thinking that makes a release safe. Running the actual deploy, and owning the go/no-go, stays with you.

Part of these blueprints

Complete build journeys that include this workflow as a stage.

Blueprint Build a SaaS MVP with AI The full path from idea to a shipped SaaS MVP — define and scope the requirements, design the architecture, API, and data model, then build it reviewed, tested, secured, cost-controlled, and deployed. View Blueprint → Blueprint Build an API Backend with AI The full path to a backend you can put clients on — define the requirements, design the architecture, API contract, data model, and access control, then build it reviewed, tested, secured, and shipped. View Blueprint → Blueprint Build a Company Website with AI The full path to a business website that holds together — plan the content, structure the site, design the components, write the page copy, then ship it as one coherent whole. View Blueprint → Blueprint Modernize a Legacy Codebase with AI The full path to taming an inherited codebase — understand it, document its architecture, pin its behavior with tests, then refactor, modernize, review, speed up, and ship it without breaking what works. View Blueprint → Blueprint Build a Documentation Website with AI The full path to docs people can actually navigate — plan the doc set, structure the site, write the guides and the API reference, then ship it as a coherent documentation site. View Blueprint → Blueprint Build an AI Workflow Automation System with AI The full path to automation that survives the real world — wire the integrations and triggers, design the control API, move the data through validated stages, evaluate the AI steps, then deploy. View Blueprint → Blueprint Build a Customer Support System with AI The full path to a support operation, not just a bot — stand up the knowledge base, route the tickets, add the AI agent, integrate your stack, close the feedback loop, evaluate, and deploy. View Blueprint → Blueprint Build a Knowledge Base with AI The full path to knowledge that's findable by people and AI — plan the taxonomy, structure it for search, write the articles, tag the metadata, make it retrievable, then ship it maintainable. View Blueprint → Blueprint Build a Marketplace with AI The full path to a two-sided platform — define the buyer-and-seller requirements, model the data, design the API, build roles and permissions, wire integrations, design the UI, then test, secure, and ship it. View Blueprint → Blueprint Build a CRM with AI The full path to a CRM that fits your sales process — define the contacts, deals, and pipeline, model the data that ties them together, then build the roles, integrations, and pipeline UI, and ship. View Blueprint → Blueprint Build an E-commerce Store with AI The full path to a store you own end to end — model the catalog and orders, design the storefront and checkout, add customer accounts and payments, then secure it, test it, and ship. View Blueprint → Blueprint Build a Data Pipeline with AI The full path to a pipeline that moves data without corrupting it — design the ingestion and transforms, extract and structure the sources, gate the quality, store it, then deliver and ship it monitored. View Blueprint →

Where to go next

Recommended next workflow AI Production Incident Workflow Work a live production incident in the right order — triage and stabilize first, then find the cause, then write the summary and postmortem — so the fire is out before the writeup begins. Use when Something is broken in production right now and you need to triage, find the cause, and write the postmortem. Start this workflow

Related workflows

Coding Workflows AI Test Generation Workflow Build a test suite that fails for real reasons, not green decoration — coverage across unit, integration, and edge cases, then a review for the gaps. Use when A file or module has little or no test coverage and you want tests that catch real bugs. View Playbook → Coding Workflows AI Security Review Workflow Review code for what an attacker would do, not just what tests catch — anchor the model as a security engineer, run a threat-focused review, then back the findings with auth and input tests. Use when You're reviewing code where a vulnerability would actually hurt — auth, input handling, anything exposed. View Playbook →

Tip: Each step's resource opens its tool pre-filled — start at step one and carry the output forward.

All playbooks