Atlassian Intelligence fix JQL errors: A 2025 overview

Let's be real: Jira Query Language (JQL) is a bit of a love-it-or-hate-it situation. It’s incredibly powerful for hunting down the exact issues you need, but the syntax can feel like you're trying to learn a whole new language on the fly. For folks who aren't developers, it can be a total non-starter. For the rest of us, it often turns into a frustrating loop of writing a query, watching it fail, and then heading to Google for answers.

Atlassian is trying to iron out some of these wrinkles with Atlassian Intelligence, their big push into AI features. One of the newest tools on the block, which is still in beta, is designed to automatically fix JQL errors.

So, is this the magic wand for all our JQL woes? Let’s take a practical look at how the feature actually works, what it’ll cost you, where it doesn’t quite hit the mark, and how it compares to a different way of thinking about AI altogether.

What is JQL?

Think of JQL as Jira's own special search code. It's a structured way to ask Jira for things using a mix of fields (like "status" or "assignee"), operators ("=" or "!="), values (""Done"" or "currentUser()"), and keywords ("AND" or "OR").

It’s the engine that powers all those custom filters, slick dashboard gadgets, and in-depth reports. When you need to get super specific, like finding "all unresolved bugs in this sprint that were supposed to be finished in the last one", JQL is how you do it.

But it’s rarely that simple. People have wrestled with its steep learning curve for years, and it can get pretty flaky with more complex searches. For example, trying to find all issues between two specific release versions sounds easy, but building a reliable JQL query for it can be a real headache. It's exactly this kind of struggle that makes an AI assistant sound so good on paper.

How Atlassian Intelligence fixes JQL errors

Atlassian’s AI features for JQL are only available for Jira Cloud Premium and Enterprise customers, and they really do two main things.

First, you’ve got the natural language to JQL translator. The idea here is that you can type something in plain English, like "my open tasks," and the AI will spit out the proper JQL for you.

The second feature, and the one we’re focusing on today, is the AI JQL fixer. This new beta tool doesn't write the query for you from scratch. Instead, it looks at a query you’ve already written, finds the syntax mistakes, and suggests how to fix them. It's basically a spell-checker for your JQL.

How well does the JQL fixer work?

Let's get into the nitty-gritty of how this JQL fixer performs, what it’s good at, and where the cracks begin to show.

How the JQL fixer works

The process is pretty straightforward. You write your JQL query and hit search. If you’ve messed something up, Jira gives you the usual error message. But now, right next to it, you'll see a new "Fix error" button.

When you click it, Atlassian Intelligence takes a look at your query and offers up a corrected version. You can accept the fix and run the search again. It's built to catch the common, simple mistakes, such as:

• Simple typos: "staus" instead of "status".

• Incorrect operators: using "=" when you really needed "IN".

• Wrong function syntax: writing "currentUser" instead of "currentUser()".

The upside: A helping hand for Jira newbies

If you're just starting to dip your toes into JQL, this feature can be a nice little assistant. It makes the whole process a bit less intimidating and might save you a few minutes you would have spent banging your head against the wall over a simple typo. It can act as a gentle guide, teaching you the correct syntax by showing you where you went wrong. Think of it as a decent set of training wheels.

The reality check: Current limitations

But as soon as you feel comfortable enough to take those training wheels off, you’ll probably start noticing the limitations. This feature is more of a patch on a tool for power users, not a real solution to the bigger business problem: getting fast, accurate answers without needing a degree in Jira.

And based on feedback from folks in the Jira community, those limitations show up pretty quickly.

• It only fixes simple syntax. The fixer isn’t going to help you build a complicated query from the ground up or point out when your logic is flawed. If your query is technically correct but doesn’t actually find what you're looking for, the AI can't help you. You still have to know which fields and functions to use in the first place.

• <quote text="When people need help with the really complex stuff, the very reason they'd turn to AI for help, the natural language-to-JQL generator is often described in community forums as "absolutely terrible"" sourceIcon="https://www.iconpacks.net/icons/2/free-reddit-logo-icon-2436-thumb.png" sourceName="Reddit" sourceLink="https://www.reddit.com/r/jira/comments/1e7ihwj/jira_cloud_ai_experiences/">

• It’s a band-aid, not a cure. At the end of the day, these AI features are designed to make a difficult tool a little less difficult. They don't change the fact that you still have to operate in a "search-first" world, manually crafting queries to hunt down the information you need.

Atlassian Intelligence pricing breakdown

Atlassian Intelligence isn't a feature you can just flick a switch to turn on. Getting access means signing up for their pricier plans, and even then, the costs and limits can be a bit confusing.

Hidden costs

To even get your hands on the JQL fixer, your team needs to be on a Jira Standard, Premium, or Enterprise plan, which is already a significant jump from the Free plan.

But the more powerful AI features, the ones that promise to do more than just fix typos, are part of Atlassian's new, bigger AI product called Rovo. And according to users in the early access program, Rovo comes with its own steep price tag, somewhere around $24 per user, per month.

On top of that, Atlassian has rolled out a system of "AI credits" and "indexed objects" that cap how much you can use the AI each month. It's a confusing model that can lead to unexpected limits, making it tough to budget for or count on the tool being available when you need it.

Plan	Price (per user/month, annual)	Key AI Features	Limitations
Free	$0	None	N/A
Standard	$7.91	Basic Rovo Search, some AI features	25 AI credits/user/month
Premium	$14.54	More AI features & credits	70 AI credits/user/month
Enterprise	Contact Sales	Most AI features & credits	150 AI credits/user/month

A better way to use AI in Jira?

While Atlassian is busy trying to help you write better queries, it’s worth stepping back and asking: are we even solving the right problem here?

The problem with the query-first approach

Focusing on JQL, even with an AI helper, keeps teams stuck in a "query-first" mindset. The real goal isn't to become a JQL wizard; it's to solve problems and answer questions for your customers or colleagues instantly. Support and ITSM teams shouldn't have to spend their time digging for information buried in tickets and documents. They should have the answers delivered right to them.

An alternative: eesel AI's knowledge-first approach

This is where tools like eesel AI come at it from a completely different angle. Instead of helping you search for information one app at a time, it brings all your knowledge together to give direct answers and automate work from the get-go.

• Bring all your knowledge together. eesel AI doesn't just stick to the Atlassian world. It connects to over 100 different sources. That means it can pull information from your Jira Service Management instance and Confluence spaces, but also from past tickets in Zendesk, documents in Google Docs, and conversations in Slack. It builds a single, unified brain for your entire support operation.

An infographic showing how eesel AI unifies knowledge from multiple sources like Slack, Jira, and Google Docs to provide comprehensive answers, illustrating an alternative to how Atlassian Intelligence fix JQL errors.

• Automate without ever touching JQL. Once all that knowledge is connected, an eesel AI Agent can understand a user's question in plain English and provide a direct answer. It can resolve tickets on its own, draft replies for human agents to review, and sort incoming requests automatically. No manual JQL searches are ever needed because the AI does all the "finding" for you.

• Get started in minutes, not months. Moving your whole organization to Jira Cloud and upgrading plans just to get a simple JQL fixer is a huge project. With eesel AI, you can connect your helpdesk and other knowledge sources and be up and running in minutes. It's a truly self-serve platform, so you can start seeing value before you ever have to talk to a salesperson.

Feature	Atlassian Intelligence for JQL	eesel AI
Primary Goal	Helps users write better JQL queries.	Automates ticket resolution and assists agents.
Knowledge Sources	Limited to the Atlassian ecosystem (Jira, Confluence).	Unifies 100+ sources (helpdesks, wikis, past tickets, etc.).
User Experience	Requires users to manually write and fix queries.	Users ask questions in natural language; no queries needed.
Setup	Requires Jira Cloud Premium/Enterprise plan.	Self-serve setup, goes live in minutes.
Pricing Model	Complicated tiers with credit limits and expensive add-ons.	Transparent, predictable plans with no per-resolution fees.

The verdict: Is the JQL fixer worth it?

So, let’s circle back to the main question: is the Atlassian Intelligence fix JQL errors feature a big deal?

For new Jira users who happen to already be on a pricey Premium or Enterprise plan, it's a minor, nice-to-have tool. It might save you a few minutes of frustration here and there. But it doesn't really touch the bigger problems that modern support and ITSM teams are dealing with every day: cutting down resolution times, deflecting common questions, and freeing up agents from tedious, manual work.

The real value of AI comes from breaking down the walls between your different information sources and automating the work of finding answers. It's not about making a clunky query language a tiny bit easier to use. For teams that are serious about improving their support, the focus shouldn't be on a small tool for fixing syntax. It should be on a complete AI platform that connects all your knowledge to deliver instant, accurate help.

Ready to move beyond fixing queries and start automating resolutions? See how eesel AI connects your entire knowledge base to deliver instant, accurate support.