Your backlog grows faster than your team can sort it. Bug reports pile up. Meanwhile, Slack threads hold half your decisions. So what if your project tool could read all of that and do the busy work for you? That is the promise of Linear AI.
Linear began as a quick, clean issue tracker for software teams. Now, however, it has become a home for AI agents too. In this guide, we explain what Linear AI is, walk through its key features, and show how it works step by step. Additionally, you will discover its pricing and limitations. By the end, you will know if it fits your team.
What Is Linear AI?
First, a quick but key point. Linear AI is not a separate product. Instead, it is the common name for the AI features built into Linear, the project tool many software teams use to plan and track work. So when people search for “What Is Linear AI?”, they usually mean Linear Agent and the smart tools around it.
To see why the tool matters, look at the company behind it. Karri Saarinen and two co-founders started Linear in 2019. The startup grew with almost no marketing. In fact, it has been profitable since its second year.
By August 2026, it passed $100 million in annual recurring revenue and had about 40,000 paying customers, including OpenAI, Cursor, Coinbase, and Ramp. That same month, an employee share sale valued Linear at $2.5 billion, double its 2025 price.
Meanwhile, Linear’s leaders made a bold bet on AI. In March 2026, Saarinen said issue tracking as we know it is dead. His point was simple. Agents now do much of the routine work, so the tool must help people and agents work side by side.
As a result, Linear launched Linear Agent that month. It is a built-in assistant that knows your roadmap, your issues, and your code.
Here is the angle most guides miss.
Linear AI is less about “chat with your tickets” and more about shared context. Coding tools like Claude Code and Cursor are great for writing code. However, they don’t know your priorities.
Linear AI tries to be the layer that tells every agent what to build and why it matters now. In other words, it is a coordination tool first and an AI tool second.
So, in plain words, Linear AI is a set of AI helpers built into Linear. They sort new issues, answer questions, draft specs, run automated workflows, and even write code.
Above all, they draw from one place: the work your team already tracks in Linear. That single source is what makes the answers useful, not generic.
6 Key Linear AI Features You Should Know
Linear has shipped AI features fast in 2026. Below are the six that matter most. Each one solves a different problem, so you can adopt them one at a time.
1. Linear Agent
Linear Agent is the heart of Linear AI. You can chat with it in the desktop or mobile app, tag @Linear in any comment, or call it from Slack and Microsoft Teams.
For example, it can turn a Slack thread into issues, find related tickets for a new project, or draft a spec from customer requests. It can also flag work that slipped while you were away.
Better still, you can save a beneficial chat as a reusable skill and run it later with a slash command.
2. Triage Intelligence
New issues land in a Triage inbox. Triage Intelligence then checks each one against your past issues. Next, it suggests likely duplicates, related issues, the right team, an assignee, and labels.
As a result, your triage lead spends minutes, not hours, on routing. This AI issue triage tool needs the Business or Enterprise plan.
3. Code Intelligence
This feature links the agent to your GitHub code. So a product manager can ask how a feature works or who owns a system, without pinging an engineer. It is also available on Business and Enterprise.
4. Loops (Agent Automations)
Loops enable the agent to autonomously respond to events. For instance, when a new issue enters triage, a loop can add a summary of customer impact. Linear renamed this feature from “agent automations” to Loops in July 2026. It runs on AI credits.
5. Coding Sessions
In June 2026, Linear Agent learned to write code with Claude Code and Codex. You assign an issue to the agent, and it works in the cloud. When it finishes, you review a diff.
Notably, Linear says it uses this method on its bugs and fixes about three out of ten incoming reports this way.
6. Agent Platform and MCP
Finally, Linear treats outside agents like teammates. You can assign issues to tools such as Devin or Codegen, just as you would to a person.
Plus, the Linear MCP server lets AI assistants like Claude read and update your Linear data. This matters because coding agents already sit in 75% of Linear’s enterprise workspaces, according to a 2026 DevClass report.
How Linear AI Works Behind the Scenes
Now, let’s examine the inner workings. You don’t need a technical background to follow the process. Basically, Linear AI works in three layers: context, permissions, and action.
Context: Everything Lives in One Place
First, Linear AI reads what your team already stores in Linear. That includes teams, projects, cycles, issues, comments, docs, and activity history. With Code Intelligence on, it also reads your linked GitHub code.
Because all this data sits in one connected system, the agent can link a customer request to a project, then to a bug, and finally to the code behind it.
Permissions: It Sees What You See
Second, the agent follows each user’s access rights. So if you can’t open a private team, the agent can’t either. This keeps sensitive work safe.
It also means two people may receive different answers to the same question. Furthermore, Linear turns the agent on by default, but admins can switch it off in settings.
Action: From Suggestion to Shipped Code
Third, Linear AI acts at three levels of trust:
- Suggest: Triage Intelligence proposes a team or label, and a human accepts it.
- Draft: Linear Agent writes a spec or project update for you to edit.
- Do: Loops and coding sessions run tasks on their own, then hand back results for review.
Here is a real-world flow.
- A customer reports a bug in Slack. Someone tags @Linear, and the agent creates an issue.
- Next, Triage Intelligence routes it to the right team and flags a duplicate.
- Then a coding session pulls error data from Sentry through MCP, traces the root cause, and returns a diff.
- Finally, an engineer reviews it and ships the fix. In short, the human stays in charge, but the busy work fades away.
Our take: The smartest way to think about Linear AI automation is as a trust ladder. Start at “suggest”. Then move up only when the agent proves itself with your data. Teams that jump straight to full automation often end up cleaning up messy tickets later.
How to Start Using Linear AI in 5 Steps
Ready to try it? Good news: you can start on any plan, even free. Follow these simple steps to set up Linear AI without chaos.
Step 1: Pick One Painful Workflow
First, don’t switch everything on at once. Instead, choose one problem, such as messy bug intake or slow sprint planning. That way, you have a clear way to measure results. For example, note how long it takes today to route ten new bugs. Then compare that time again after two weeks.
Step 2: Ask Linear Agent Real Questions
Next, press Cmd or Ctrl + J in the desktop app to open the chat. Then ask something you would normally ask a teammate, like, “What’s blocking the Q4 launch?” Check whether the answer matches reality. If it doesn’t, your issues may lack detail, and that is useful to know too.
Step 3: Connect Slack, GitHub, and Your Support Tool
After that, link your key tools. Slack lets people send requests with @Linear. GitHub gives the agent code context. Meanwhile, tools like Zendesk or Intercom bring the customer’s voice into Linear.
Step 4: Turn On Triage Intelligence
If you’re on Business or Enterprise, enable Triage Intelligence. Initially, please review each suggestion manually. Then, once it’s right most of the time, let it set some fields for you.
Step 5: Save Skills, Then Add Loops
Finally, save your best prompts as skills so the whole team can reuse them. Later, add one Loop or try coding sessions on small, clear bugs. Additionally, monitor AI credit usage in the usage dashboard.
Pro tip: Write better issues. AI agents, like new hires, only do excellent work with clear briefs. So add the goal, the user impact, and a link to the source chat. As a result, every agent you plug in gets smarter overnight. Good AI starts with good tickets.
Linear AI Pricing, Limits, and Who It’s For
Let’s talk money. As of now, Linear AI pricing for yearly billing is as follows:
- Free: $0, with Linear Agent, skills, and MCP access
- Basic: $10 per user per month, plus coding sessions and Loops on AI credits
- Business: $16 per user per month, adding Triage Intelligence, Code Intelligence, and Linear Asks
- Enterprise: custom pricing with advanced security controls
Coding sessions and Loops draw from prepaid AI credits on top of seat fees. So budget for usage, not just seats. A small team that runs a few coding sessions a week will spend far less than one that automates every bug. Prices change often, so check Linear’s pricing page before you buy.
However, Linear AI has limits. It only knows what lives inside Linear and in your linked code. For example, a decision made in a Slack channel stays hidden unless someone tags @Linear or copies it into an issue.
Likewise, promises from customer calls and emails don’t reach the agent by default. Because of this limitation, a coding agent may build exactly what the ticket says and still miss what the customer wanted.
So who should use it? It fits best if you are:
- A startup or scale-up that already runs product work in Linear
- An engineering team buried under bug reports
- A product team that wants specs and updates drafted faster
On the other hand, it fits poorly if your team lives in Jira or works mostly outside software. In that case, Atlassian’s own AI tools may suit you better.
Here’s a fresh angle. Most reviews compare Linear AI to Jira’s AI. Yet the real rival is the copy-paste habit. Every time someone pastes context from Slack into a chatbot and then pastes the result into a ticket, they lose details.
The tool wins when it removes those steps. Therefore, judge it by one simple test: does it cut the number of tools your team copies between?
Conclusion: Is Linear AI Worth It?
So, what is Linear AI? In short, it is the set of AI tools built into Linear. Linear Agent answers questions and drafts work. Triage Intelligence sorts new issues. Code Intelligence explains your codebase. Meanwhile, Loops and coding sessions take on tasks by themselves and hand back results for review.
The real strength of Linear AI is not any single feature. Rather, it is shared context. Your people and your AI agents read the same roadmap, the same issues, and the same code. As a result, fewer tasks fall through the cracks, and less time goes into copying notes between tools.
Still, it isn’t magic. The agent only knows what sits inside Linear, so clear issues and linked tools matter a lot. Furthermore, AI credits can add up, so watch usage as you scale.
Here is our advice. Start small. First, open Linear Agent and ask it three real questions about your current sprint. Then, if the answers help, connect Slack and GitHub and try Triage Intelligence for two weeks. Finally, measure the time saved before you add more automation.
Frequently Asked Questions
1. Is Linear AI free to use, or do you need a paid Linear plan for it?
Every plan, including the free one, includes Linear Agent and its associated skills. However, Triage Intelligence and Code Intelligence require the Business plan. Meanwhile, coding sessions and Loops use prepaid AI credits on basic and higher plans.
2. What is the difference between Linear Agent and Triage Intelligence?
Linear Agent is a chat assistant you can question and give tasks to anywhere in Linear. By contrast, Triage Intelligence works quietly in the Triage inbox and suggests duplicates, teams, assignees, and labels for each new issue.
3. Can Linear AI write code and fix bugs on its own for software teams?
Yes. Since June 2026, coding sessions have allowed Linear Agent to write code in the cloud using Claude Code or Codex. You assign an issue, the agent works, and then an engineer reviews the diff before anything ships.
4. Does Linear AI work with Slack, GitHub, and other tools teams use?
Yes. You can tag @Linear in Slack and Microsoft Teams, link GitHub for code context, and connect support tools like Zendesk. Plus, the Linear MCP server lets outside AI assistants read and update your Linear workspace.
5. Is Linear AI safe to use with private projects and sensitive data?
Linear AI follows each user’s existing permissions, so it only sees what you can see. Admins can also turn the agent off in settings. Still, review your company’s data rules before you give it code access.
Author & Researcher with 8 years of experience across IT and non-IT industries, delivering insightful and data-driven content.
