Currently in development. Marketplace listing coming soon.

Turn vague Jira issues into definition-ready work orders.

Definition of Ready AI for Jira scores issue quality, detects ambiguity, generates acceptance criteria, creates QA checklists, and produces structured implementation briefs before work starts.

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Jira issue readiness

Improve password reset flow

Status: Needs clarification

62 / 100

Readiness score

Findings

  • Critical

    Missing acceptance criteria

  • Warning

    Expected behavior is unclear

  • Warning

    Test plan is missing

  • Warning

    Security-sensitive area detected

Generated clarifying questions

  • What specific password reset error states should be improved?
  • Should expired and invalid reset links show different messages?
  • What automated tests should be added?
  • Is account enumeration a concern for this change?

Generated AI work order

Objective: Improve the password reset error flow so users receive clear, safe guidance when reset links are invalid or expired.

Acceptance criteria

  • • Expired links show a clear message.
  • • Invalid links do not expose whether an account exists.
  • • Users can request a new reset email.

Test plan

  • • Unit tests for token expiry.
  • • Request tests for invalid links.
  • • System test for user-facing reset flow.

AI coding agents need better tickets.

Many Jira issues are too vague to safely hand to a developer or AI coding agent. They lack acceptance criteria, test expectations, scope boundaries, affected components, risk notes, or rollout guidance. When the ticket is unclear, the implementation becomes guesswork.

Product managers need clearer requirements.
Developers need fewer ambiguous handoffs.
QA teams need testable acceptance criteria.
AI coding agents need structured instructions.
Engineering leads need safer sprint commitments.

Everything needed to make an issue ready.

1

Readiness score

Score each issue against configurable Definition of Ready rules.

2

Ambiguity detection

Find vague language, missing decisions, and unclear expected behavior.

3

Acceptance criteria generator

Convert rough requirements into testable pass/fail criteria.

4

QA checklist generator

Create practical test expectations before implementation starts.

5

Risk detection

Flag work involving auth, permissions, payments, migrations, external APIs, and sensitive data.

6

AI work order

Generate a structured prompt-style work order for human developers or AI coding agents.

7

Approval-gated updates

Write generated work orders back to Jira only after explicit user approval.

8

Project rules

Configure readiness requirements per project.

9

Usage and audit trail

Track analysis usage, quota state, and approved Jira write actions.

10

Sprint readiness path

Use Advanced workflows for bulk readiness reviews as the product matures.

What the readiness check looks for.

acceptance criteria
expected behavior
scope boundaries
test plan
component or system context
dependency notes
rollout and rollback notes
auth, payment, migration, data, and external API risks

From vague ticket to clear work order

1

Open Jira issue

Start from the issue your team already uses.

2

Run readiness analysis

Check the issue for missing context, ambiguity, and risk.

3

Review findings

See what blocks the issue from being safely started.

4

Answer clarifying questions

Use generated questions to improve the ticket.

5

Generate work order

Create a structured implementation brief with acceptance criteria and test plan.

6

Approve Jira update

Add the work order to Jira only after human review.

Example: before and after

Before

Improve password reset flow.

Users are confused by password reset errors. Make the flow better.

Findings

  • • Missing acceptance criteria
  • • Expected behavior unclear
  • • Test plan missing
  • • Security-sensitive area detected
  • • Scope boundary missing

After

Generated work order

Objective: Clarify and improve the password reset error flow so users understand when a reset link is invalid or expired and can request a new link.

Acceptance criteria

  • • Expired reset links show a clear error message.
  • • Invalid reset links do not reveal whether an account exists.
  • • Users can request a new reset email.
  • • Valid reset links continue to work as expected.
  • • Password reset behavior is covered by automated tests.

Test plan

  • • Test expired token behavior.
  • • Test invalid token behavior.
  • • Test valid token behavior.
  • • Test user-facing error copy.
  • • Test that account enumeration is not introduced.

Risk: Authentication-sensitive change. Requires review before implementation.

Built for Jira, with server-side AI controls.

Definition of Ready AI for Jira works before implementation begins. It analyzes Jira issue content, not repositories or pull requests. Valuable prompts, model routing, and provider keys stay in the ProcessLayer backend.

No repo cloning
No source-code access in v1
No provider keys in Forge
No autonomous Jira changes
Explicit approval before Jira writes
Forge UI with Rails-owned prompts
Minimal Jira scopes
Clear AI/data disclosure

Architecture at a glance

1

Forge surfaces

Issue panel, issue action, project settings, and global page run inside Jira.

2

Rails backend

Accounts, installations, prompts, model routing, usage, reports, and audit logs live in ProcessLayer Core.

3

Forge Remote

The backend verifies Forge invocation requests before resolving tenant, license, edition, and quota.

4

Human approval

Forge writes comments or subtasks only after an explicit user action and entitlement check.

Editions

Paid plans are intended to be sold through Atlassian Marketplace. Final pricing will be published with the Marketplace listing.

Unlicensed / demo

  • • Product UI and sample/demo analysis
  • • Readiness concepts and preview experience
  • • Real Jira issue AI generation blocked until trial or paid license

Standard

  • • Issue readiness analysis
  • • AI work order generation
  • • Acceptance criteria and QA checklist
  • • Risk detection
  • • Project settings
  • • Approved Jira comment writing
  • • Audit log
  • • 1,000 analyses per month target

Advanced

  • • Everything in Standard
  • • Bulk and sprint readiness workflows
  • • Custom rules
  • • Advanced prompt profiles
  • • CSV/export path
  • • Team and release-readiness dashboards
  • • 10,000 analyses per month target

Edition names, quotas, and final prices are subject to Atlassian Marketplace configuration before launch.

FAQ

Does the app access source code?

No. The v1 app analyzes Jira issue data only. It does not clone repositories, inspect branches, or access pull requests.

Does the app send data to external AI providers?

When AI generation is configured, selected Jira issue text may be sent from the ProcessLayer backend to an AI provider to produce readiness reports and work orders. Provider API keys and prompts are not exposed to Forge or the browser.

Can the app automatically modify Jira issues?

No autonomous changes. Any Jira write action, such as adding a generated work order as a comment or creating clarification subtasks, requires explicit user approval.

Is this a replacement for test management tools?

No. It helps improve issue quality and readiness before work starts. It can complement QA and test-management tools.

Is this only for AI coding agents?

No. It helps human developers, QA teams, product managers, and engineering leads prepare better work.

Is the app available now?

The product is currently in development. The Marketplace listing will be added when available.

Prepare Jira issues before work starts.

Generated outputs should be reviewed by humans before they are used for implementation or written back to Jira.

Join early access