Every software project usually starts with a simple idea.

“We should build this.”

Then the questions begin.

What exactly should we build?

What systems will it affect?

What are the dependencies?

What does “done” actually mean?

Which team owns it?

What could go wrong?

And perhaps the biggest question today:

Is the work clear enough for an AI coding agent to execute?

This is where Jira Planner is becoming interesting for modern engineering teams.

Atlassian has introduced Jira Planner to help teams turn rough ideas into structured, agent-ready plans before development begins. It is currently in Early Access for eligible Jira Cloud customers.

The Problem Is Not Always Coding

AI coding tools can already generate code, fix bugs, create tests, and work on development tasks.

But giving an AI agent a vague requirement can create a different problem.

Imagine a Jira work item says:

“Improve the customer checkout experience.”

A developer may immediately ask:

• Which part of checkout?
• Which application?
• What customer problem are we solving?
• Which APIs are involved?
• Are there architectural constraints?
• What existing decisions should we follow?
• How will we measure success?

If those details are missing, an AI agent has to make assumptions.

And when the assumptions are wrong, the team may spend more time correcting the work later.

Atlassian describes this as an intent problem: the challenge is not simply generating code, but giving the agent enough context to understand what the team actually intends to build.

What Does Jira Planner Do?

Jira Planner helps teams move from a rough idea to a structured plan.

Instead of asking teams to create a perfect specification first, Planner can ask follow-up questions to clarify:

Scope

Constraints

Dependencies

Expected outcomes

Success criteria

It then uses context from connected repositories and the Atlassian Teamwork Graph to make the plan more relevant to the organization’s actual technology environment and previous decisions.

The result is not simply another AI-generated document.

It is a plan designed to become actionable work.

Use Case 1: Planning a Multi-Sprint Feature

Consider an enterprise building a new customer self-service capability.

The initial idea may be simple:

“Allow customers to manage their subscriptions online.”

Planner can help the team work through the details before development begins.

The resulting plan can capture:

Business requirement → Architecture → Dependencies → Acceptance criteria → Jira work items

The team can review the plan before development starts rather than discovering important gaps halfway through the project.

Use Case 2: Working Across Multiple Repositories

Large enterprises rarely have everything inside one codebase.

A feature may involve:

• Front-end applications
• Backend services
• APIs
• Authentication systems
• Shared libraries
• Multiple repositories

Jira Planner can use context from connected repositories, including multi-repository environments, helping teams build plans around the technology landscape they actually have.

That can be particularly useful for complex engineering initiatives where changing one component may affect several others.

Use Case 3: Get Product, Engineering and Design Aligned

Another common problem is that planning happens separately.

Product creates requirements.

Engineering interprets them.

Design creates user flows.

Developers later discover missing details.

Jira Planner creates editable Confluence Live Docs where engineers, product managers, and designers can collaborate, comment, and work through trade-offs before the plan is finalized.

This brings an important step forward:

Alignment happens before execution—not after problems appear.

Use Case 4: Turn the Plan Into Jira Work

Once the team agrees on the plan, Jira Planner can break it into sequenced Jira work items with dependencies and acceptance criteria.

This creates a much clearer path:

Idea → Plan → Team Review → Jira Work → Agent Execution

That connection is important because the planning process does not end in a document that sits somewhere disconnected from delivery.

The plan becomes part of the work itself.

Why This Matters for Enterprise Teams

The rise of AI coding agents does not eliminate the need for good planning.

In many ways, it makes planning more important.

When development becomes faster, unclear requirements can also move faster.

A poorly defined requirement can result in an agent producing the wrong implementation quickly.

That is why enterprises need a better connection between:

Business intent

↓

Technical context

↓

Team alignment

↓

Execution

Jira Planner is designed around this connection.