Eval directory

Evals for Atlassian

Eval coverage for Atlassian, mapped from its public product surface.

About Atlassian

Atlassian sells collections of apps and AI agents on a single teamwork platform, with the Product Collection bundling Feedback, Jira Product Discovery, and Rovo. It captures customer feedback across channels, uses AI to organize and surface themes, and turns priorities into roadmaps connected to delivery in Jira. Other collections cover teamwork, strategy, service, and software delivery; the Product Collection is offered via a waitlist.

Industry

AI-native product management and team collaboration platform

Website

www.atlassian.com

Use the eval library for Atlassian

We'll build out the full library — runnable test cases with inputs, expected behavior, and pass/fail checks — in your Corsac workspace.

All evals →

More AI eval libraries

Coverage map

What would you measure for Atlassian?

6 scoring areas · 23 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

Adversarial robustness
Workflow quality
Safety gates
Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

Customer feedback capture

Automatically capturing feedback at scale across the channels the Product Collection page names — support, sales, calls, chat, and surveys — so product input arrives in one place rather than scattered across tools.

“Turn scattered tools into a seamless system for AI orchestration, planning, knowledge, and delivering work at scale.” www.atlassian.com

Mapped capabilities

4 capabilities

Multi-channel intake
Feedback arriving from support, sales, calls, chat, and surveys as described on the Product Collection page.
Automatic capture at scale
Capture presented as automatic and high-volume rather than manual entry.
Channel coverage boundaries
Distinguishing named channels from channels not stated in the source material.
Feedback as shared product signal
Positioning captured input as signal available to product teams, not a support-only artifact.

AI insight organization

Using AI to organize, tag, and surface clear themes from captured feedback so teams focus on meaningful signal instead of noise, per the 'Make customer insight actionable' claim.

Mapped capabilities

4 capabilities

Theme surfacing
Grouping raw feedback into themes that a product team would act on.
Organizing and tagging
AI-applied structure and tags over incoming customer input.
Signal versus noise framing
Prioritizing insights that matter over undifferentiated volume.
Insight-to-decision handoff
Themes feeding the prioritization surface rather than terminating as a report.

Prioritization and roadmapping

Jira Product Discovery as the dedicated place to gather insights, prioritize what matters, and build living roadmaps that align teams — with prioritization framed as transparent and evidence-based.

“In Jira, teams and AI agents plan, execute, and deliver outcomes together.” www.atlassian.com

Mapped capabilities

4 capabilities

Idea and insight gathering
A dedicated space collecting insights ahead of a prioritization decision.
Evidence-based prioritization
Tying feedback and product signals to prioritization choices transparently.
Living roadmaps
Roadmaps that stay current and align teams rather than static plan documents.
Stakeholder alignment
Roadmap output serving as the shared reference across teams.

Discovery-to-delivery connection

Connecting prioritized product bets to delivery in Jira, the seam the Product Collection page repeatedly names as what distinguishes it from standalone feedback or roadmap tooling.

Mapped capabilities

3 capabilities

Roadmap-to-Jira linkage
Priorities connecting to delivery work in Jira as stated on the collection page.
Single connected place
Insights, prioritization, and delivery described as brought together in one place.
Traceability from feedback to build
Following a customer signal forward to the work it justified.

Illustrative example

Input: Support and sales keep raising the same complaint. Walk me through how the Product Collection takes that from raw feedback to work my engineers actually pick up.
Expected behavior: Describes the ordered path: Feedback captures input across channels, AI organizes it into themes, Jira Product Discovery prioritizes it against evidence and places it on a roadmap, and that roadmap connects to delivery in Jira.

Rovo across product workflows

Rovo applied across the product workflow from discovery to delivery — the collection page names analyzing feedback, reviewing priorities, drafting PRDs, and generating release notes.

“Use AI to analyze feedback, review priorities, draft PRDs, generate release notes” www.atlassian.com

Mapped capabilities

4 capabilities

Feedback analysis
AI analysis over collected customer feedback.
Priority review
AI assistance reviewing an existing set of priorities.
PRD drafting
Drafting product requirements documents as a named Rovo task.
Release notes generation
Generating release notes as work moves toward delivery.

Collection scope and access

What the Product Collection contains, how it sits against Atlassian's other collections, and how a prospective customer reaches it — the collection is offered via a waitlist, with separate product-advice and enterprise contact paths.

Mapped capabilities

4 capabilities

Bundle composition
Product Collection as Feedback, Jira Product Discovery, and Rovo.
Collection differentiation
Distinguishing Product from the Teamwork, Strategy, Service, and Software collections and their named apps.
Waitlist availability
Access via 'Join the waitlist' rather than immediate self-serve signup.
Buyer contact routing
Product advice and enterprise contact forms as the stated inquiry paths.

Illustrative example

Input: What apps come with Atlassian's Product Collection, and can I sign up for it today?
Expected behavior: Names exactly the three included apps — Feedback, Jira Product Discovery, and Rovo — and states that access is via a waitlist rather than immediate self-serve signup.

Coverage is mapped from Atlassian's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for Atlassian test?+

The coverage map is generated from Atlassian's own public product surface (AI-native product management and team collaboration platform): 6 scoring areas — Customer feedback capture, AI insight organization, and Prioritization and roadmapping, and more — spanning 23 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the Atlassian evals scored?+

Every case generated for Atlassian — across Customer feedback capture and AI insight organization and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the Atlassian library include?+

The full Atlassian library is built on request. The coverage map spans 6 areas and 23 capabilities (for example, Multi-channel intake and Automatic capture at scale under Customer feedback capture); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against Atlassian or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped Atlassian areas and set them up in a Corsac workspace, where you can run every test case against Atlassian or your own agent with your own data.