A practical guide to Jira Service Management agentic AI

For a while, "AI" felt like a buzzword you could slap on anything. But now, it's starting to do some real work, especially in IT Service Management (ITSM). Atlassian is making a big move here with its Jira Service Management agentic AI, which runs on the Atlassian Intelligence engine and a new set of AI helpers called Rovo agents.

So, what does this actually mean for your team? In this guide, we'll give you a look at what Atlassian's agentic AI is, what it does, how much it costs, and its considerations. We'll break it down so you can decide if it’s the right call for your team, or if a tool-agnostic AI teammate might be a helpful addition to how you already work.

What is Jira Service Management agentic AI?

First off, what's "agentic AI" anyway? Put simply, it’s AI that doesn't just fetch information, it actually gets things done. Instead of just being a fancy search bar, an agentic AI can assess a situation, make a decision, and take action all on its own. To better illustrate this, the following graphic shows how agentic AI differs from a standard chatbot.

Think of it as an AI that can handle a task from start to finish, like granting software access or triaging an incident, rather than just pointing you to a help article.

Atlassian's version of this is built around Rovo, their AI-powered assistant. The technology behind Rovo is the Teamwork Graph, which connects and understands all the data floating around in your Atlassian tools, like Jira and Confluence. The goal is to create a smart layer over the tools you use every day, giving the AI a deep understanding of your company's projects, teams, and internal knowledge.

The promise is a native AI that understands how your company works and can speed up IT operations and employee support. This functionality is exceptionally effective for teams integrated within the Atlassian ecosystem, offering a reliable and mature platform for your support needs.

Core features of Jira Service Management agentic AI

Atlassian’s new AI features are powerful, especially if your team is already using its products. They provide a seamless experience when your data and workflows are housed within that ecosystem.

For IT operations and incident management

When things go wrong, the last thing you need is more noise. Jira's AI cuts through the chaos with several key features designed for your IT ops team.

• Key Features:

  • AI-powered alert grouping: Instead of getting spammed with a hundred separate alerts for one problem, the AI intelligently groups related alerts from your monitoring tools. This helps your team focus on the actual issue, not the flood of notifications.
  • AI-assisted root cause analysis: During an incident, Rovo agents can jump in to help. They’ll dig up relevant info from your observability tools and even suggest a potential root cause, giving your incident managers a head start.
  • Automated Post-incident reviews (PIRs): Writing PIRs can be time-consuming. The AI can generate a first draft by pulling data directly from the incident timeline and connected alerts, which saves a ton of manual work.

• Complementary options: This functionality is purpose-built to leverage operational data within Atlassian's Teamwork Graph. For teams that also utilize a wide mix of external tools, a platform-agnostic AI like eesel AI for ITSM can connect to Jira and numerous other platforms to provide additional context.

The virtual agent for employee support

For handling everyday employee questions, Jira has a virtual agent designed to be the first line of defense.

• Key Features:

  • Multichannel support: The virtual agent isn't just stuck on a help portal. It can chat with employees in Slack, Microsoft Teams, and over email, meeting them where they already work.
  • Dual-response system: It handles requests in two ways. For simple questions, it uses AI answers to search your Confluence knowledge base and give a direct response. For more involved, multi-step requests like getting software access, it uses intent flows, which are pre-built conversational paths to guide the user.

• Complementary options: The AI's effectiveness is maximized when paired with a comprehensive Confluence knowledge base. While building and maintaining intent flows provides precise control over user requests, some teams may also consider eesel AI. This tool can learn directly from past tickets and conversations to complement your formal documentation.

Agent productivity features

Beyond fielding frontline requests, the AI also has features to make your human agents more efficient.

• Key Features:

  • AI summaries: If an agent gets a ticket with a long back-and-forth history, the AI can summarize the whole thread in seconds.
  • Service triage assistant: This is a Rovo agent that helps automatically categorize, prioritize, and route incoming tickets to the right team or person.
  • Service request helper: Another Rovo agent that acts as a sidekick. It can suggest the next steps on a ticket, point out the right subject matter expert to loop in, and help draft replies based on the context.

• Complementary options: These features provide AI suggestions to assist agents with high reliability. For teams that prefer to start with a "human-in-the-loop" approach, some platforms like eesel AI's Copilot can draft replies for agent review directly within Jira. This allows the AI to learn from feedback while you maintain full control.

The setup process

Atlassian offers a structured setup process that ensures its AI has deep access to the necessary data within its ecosystem.

Strength of the Atlassian ecosystem

The Teamwork Graph is the brain behind the whole operation, and its core strength lies in its deep integration with data within Atlassian's own products.

This focus ensures that for teams standardized on Jira and Confluence, the AI has a cohesive and unified understanding of company data.

• A complementary approach: For organizations using a wide variety of external software, a platform-agnostic AI can be a helpful addition. Platforms like eesel AI are designed to integrate with over 100 sources, allowing you to bring together information from various tools alongside your Jira setup.

Precise configuration of virtual agents

Getting the virtual agent to handle complex requests is a process that gives admins a high degree of control. Admins can define "intents" for every type of request and build conversational flows in a low-code editor.

This structured approach ensures that the AI responds exactly as you intend it to, which is vital for maintaining service quality.

• A complementary approach: An additional option is an "invitation" model where an AI platform, such as eesel AI, is connected to a helpdesk to learn from historical ticket data. This can offer a different setup experience to complement your manual intent flows.

Pricing and considerations

Before you move forward with Jira's AI features, it is helpful to understand the tiered plans and the capabilities of the platform.

A closer look at pricing

The advanced AI features like the Virtual Agent and AIOps are available on Jira Service Management's Cloud Premium and Enterprise plans. The pricing is per-agent, and for the virtual agent, Atlassian provides a generous number of assisted conversations to get you started.

Here's a quick breakdown:

Plan	Starting Price (per agent/mo)	Key AI Features Included	Virtual Agent
Premium	$49.05	Rovo agents, AIOps (alert grouping, PIR generation), Asset Management	Includes 1,000 assisted conversations per month, then pay-per-use
Enterprise	Billed Annually (Contact Sales)	All Premium features, plus advanced security, unlimited automation, and more Rovo credits	Includes 1,000 assisted conversations per month, then pay-per-use

Key considerations

When evaluating Jira Service Management agentic AI, keep these points in mind:

• Ecosystem focus: The AI is deeply optimized for the Atlassian suite, providing an exceptionally smooth experience for teams that centralize their work in Jira.

• Primary learning sources: The AI is built to leverage structured knowledge bases like Confluence, ensuring high-quality responses based on your official documentation.

• Scalable pricing: The model offers tiered plans to match different team sizes, allowing you to scale your AI usage as your company grows.

• Comprehensive configuration: The ability to manually define intents ensures that your virtual agent operates with precision and meets your specific business standards.

• Complementary additions: Some teams may also look at tools like eesel AI, which offers interaction-based pricing. Such tools are designed to work alongside your existing stack, including Jira, without requiring a commitment to a single ecosystem.

For a deeper dive into how Atlassian positions its AI capabilities, this video provides a direct overview from the company itself, explaining how machine learning and AI are integrated into the platform to help teams.

Atlassian's official overview of the machine learning and AI capabilities integrated into the Jira Service Management platform.

Is Jira Service Management agentic AI right for your team?

Jira's agentic AI is a deeply integrated and powerful solution for teams standardized on the Atlassian platform. If your company uses Jira and Confluence extensively, it delivers significant value by working seamlessly within that trusted environment.

It is a mature, reliable, and enterprise-grade option that powers customer service for thousands of companies worldwide. While it focuses on the Atlassian ecosystem and offers detailed configuration options, these features provide the stability and control that many support teams require.

For teams that prioritize flexibility across a diverse set of tools, exploring complementary options may also be beneficial. Tool-agnostic solutions like eesel AI are designed to work with your existing stack and can be a great way to enhance your Jira setup even further.