Your Jira AI API: What it is and how to get AI features today

Let’s be honest, you can’t escape the AI hype in project management right now. Atlassian is in on the action with its own AI, Rovo, which is now baked into Jira to help teams get more done. It’s a decent start, for sure. But the moment developers and admins get their hands on a new tool, they immediately start thinking, "Okay, this is cool, but how can we really push its limits?" They want to build their own workflows, connect it to other tools, and really make the AI work for their specific needs. To do any of that, they need a Jira AI API.

A quick scroll through community forums and public feature requests shows this isn’t just a small group of power users asking for this; it’s a huge demand. The problem? Atlassian doesn’t have a public API for its AI features yet. This leaves teams who want to build custom AI workflows feeling a bit stuck.

This guide will walk you through what Jira’s own AI can do, the very real roadblocks you hit without a Jira AI API, and how you can get the same (or even better) AI features for Jira today, without the wait.

Jira’s native AI and the missing Jira AI API

Atlassian’s AI is called Rovo AI, and you’ll find it popping up across their whole family of products, including Jira and Jira Service Management. The idea is that it can handle some of the tedious, manual work that slows everyone down.

Inside Jira, Rovo can do a few neat things straight away:

• AI Summaries: It can take a long, rambling comment thread on a ticket and boil it down to a quick summary, so you can get up to speed in seconds.

• JQL Generation: You can type what you want to find in plain English, and it will generate the proper Jira Query Language (JQL) for you. No more fighting with syntax.

• AI-powered Support (JSM): If you use Jira Service Management, Rovo acts as a virtual agent. It can answer common questions and even help your support agents write knowledge base articles in Confluence.

These features are genuinely useful, but they all lead back to the same question: how can we access this power outside of the user interface? And that brings us to the missing Jira AI API.

Despite a lot of noise from the developer community in public feature requests like AI-8, Atlassian hasn’t released a public API for Rovo. The standard Jira REST API is massive and lets you control almost everything about issues, projects, and users, but it has zero endpoints for tapping into the new generative AI stuff. For now, Jira’s AI is stuck inside the app.

The limitations of a closed AI ecosystem

So, what’s the big deal if there’s no API? It means the AI is basically locked in a box. This creates some real-world headaches that go beyond just what you can’t build; it’s about all the clever things you’re missing out on.

You can’t build custom workflows or integrations

Without an API, you have to use the AI features exactly as Atlassian designed them. You can’t bring that AI power into the other tools your team relies on every single day.

Just think about the possibilities. Your developers can’t build a simple Slack bot that pulls an AI summary of a Jira ticket when you ask for it. Your product manager can’t spin up an internal dashboard that uses AI to track ticket sentiment and flag projects that might be in trouble. These are the kinds of custom hook-ups that developers are asking for, but they’re just not possible when there’s no bridge between Jira’s AI and everything else.

This workflow illustrates how an integrated tool can create custom support automation, a capability missing without a Jira AI API.

You’re locked into Atlassian’s knowledge sources

Atlassian’s Rovo AI is trained to work best with information that lives inside its own world, which for most people means Jira tickets and Confluence pages. That’s fine if your company is 100% on Atlassian products, but let’s be real, whose is?

Most companies have information scattered all over the place. Your most important troubleshooting guides might be sitting in Google Docs, your official company policies could be in Notion, and your team’s best day-to-day advice is probably buried in Slack threads. A closed-off AI can’t see any of this, which gives it massive blind spots. The answers it gives are often generic or just plain incomplete because it’s missing the full picture.

This infographic shows how a third-party tool overcomes the limitations of a missing Jira AI API by connecting to multiple knowledge sources.

Lack of granular control and customization

Out-of-the-box AI tools often treat everyone the same. With Jira’s native AI, you can’t really tweak its personality to match your company’s voice. You can’t teach it custom tricks, like looking up an order status in Shopify or checking an internal database. You’re also stuck with its pre-set rules for when it should jump in to help.

This "black box" setup can be a real pain for teams that need to be precise about how they automate their support and project management. You end up having to work around the AI’s limits instead of shaping it to do exactly what you need.

How to get Jira AI features today

Okay, so you need AI features for Jira, but an official API is nowhere in sight. What do you do? You’ve basically got two paths: build something from the ground up yourself, or use a tool that’s already figured this out.

The DIY approach: Building a custom integration

Look, if you have a dev team with a lot of free time, you could technically rig something up yourself. It would probably involve using the standard Jira API to pull ticket info, sending it over to a service like OpenAI or Gemini, getting a response back, and then using the Jira API again to post that response as a comment.

While it’s doable, this route is paved with some pretty big potholes:

• It’s a huge time-sink: This isn’t a quick side project. It takes a lot of developer hours to build, and more importantly, to keep it running. APIs change, AI models get updated, and you’ll be the one fixing it when it breaks.

• It gets complicated, fast: Suddenly you’re managing multiple API keys, worrying about data security, and spending ages tweaking prompts to get the AI to say the right thing consistently.

• It’s not for everyone: This is a full-blown software project. It’s definitely not something a support manager or Jira admin can just set up on their own over a cup of coffee.

The integrated platform approach: A smarter alternative

A much simpler way forward is using an AI platform built to sit on top of the tools you already use, like Jira Service Management. This is exactly what a tool like eesel AI does. It gives you all the power of a custom-built solution without any of the building-it-from-scratch headaches.

• Go live in minutes, not months: You can forget about the complex DIY project. With eesel AI, you can connect your Jira Service Management account with one click. You can have a working AI agent up and running in minutes, without talking to a salesperson or writing a single line of code.

• Unify all your knowledge, instantly: eesel AI directly solves the "locked-in" problem. It connects to over 100 places where your information lives, including Jira, Confluence, Google Docs, Notion, Slack, and more. This gives your AI a complete understanding of your company’s knowledge, so the answers it provides are actually helpful.

• Gain total control with a workflow engine: The prompt editor and custom actions in eesel AI give you the fine-tuned control that native AI is missing. You can define the AI’s exact personality, set up specific rules for which tickets it should handle, and build custom actions to pull live data from anywhere, whether it’s your CRM or an internal database.

This image shows the customization and control panel that provides the granular control a native Jira AI API would ideally offer.

Comparing Jira AI pricing with an integrated solution

Of course, we have to talk about money. Figuring out the pricing for these AI tools is a big piece of the puzzle. Jira’s native AI is part of its paid plans, but that doesn’t necessarily make it the cheapest or most predictable option.

Understanding Jira’s native AI pricing

Rovo’s features are included in Jira’s plans, but your access is measured with "AI credits." Here’s a quick look at how it works, based on Atlassian’s pricing for Jira Software.

Plan	Price (per user/month, annual)	Key AI Features & Limits
Standard	$7.53	Includes Rovo Search, Chat, and Agents. Limited to 25 AI credits per user/month.
Premium	$13.53	Everything in Standard, plus more advanced features. 70 AI credits per user/month.
Enterprise	Billed Annually	Everything in Premium. 150 AI credits per user/month.

This model can be a bit tricky for a couple of reasons:

• Unpredictable Costs: What on earth is an "AI credit"? Good question. It’s a vague unit that makes it really hard to predict your monthly bill, especially if your team’s usage goes up and down.

• Per-User Model: Your costs go up as your team grows, not necessarily as your AI usage grows. You’re paying for every single person on the plan, even if most of them barely touch the AI features.

A transparent alternative: eesel AI’s pricing model

eesel AI’s pricing is designed to be much more straightforward. All the main tools, like the AI Agent, Copilot, and Triage, are included in every plan. The price just depends on how many AI interactions you have.

Plan	Price (monthly, annual billing)	Key Features & AI Interactions
Team	$239/mo	Up to 1,000 AI interactions/mo. Train on docs, Slack integration.
Business	$639/mo	Up to 3,000 AI interactions/mo. Train on past tickets, custom AI Actions, API calls, bulk simulation.
Custom	Contact Sales	Unlimited interactions. Advanced integrations and security controls.

This approach has some clear benefits:

• Predictable: The price is based on something you can actually count: AI interactions. You know what you’re paying for. No weird "credits" to figure out.

• No Per-Resolution Fees: Unlike a lot of other AI support tools, eesel AI doesn’t charge you more when the AI successfully resolves a lot of tickets. You’re not penalized for being efficient.

• Flexible: You can start with a monthly plan and cancel whenever you want. It’s a low-risk way to try it out and see if it works for you before you commit to anything long-term.

Stop waiting for a Jira AI API and start automating

The community is loud and clear about wanting a Jira AI API, but waiting around for Atlassian means you’re leaving a lot of time-saving opportunities on the table. You could be making things easier for your team right now. The limits of a closed AI system are real, stopping you from building the smart, custom workflows that your team actually needs.

While you could try to build a solution yourself, it’s a massive project that’s tough to build and even tougher to keep running. Jira’s built-in AI is handy for simple things, but it’s ultimately stuck behind the walls of its own ecosystem with a one-size-fits-all model.

An integrated platform like eesel AI gives you the best of both worlds. You get the power and control you’d want from an API, but with the ease of a tool you can set up yourself. It connects all your scattered knowledge, puts you in the driver’s seat of the AI’s behavior, and even lets you test everything out with a powerful simulation mode.

Don’t let the lack of an official API hold you back. You can start automating your Jira workflows and making your support process way better in the next ten minutes.

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