Sistava

Linear AI

AI inside Linear, the issue tracker and product roadmap tool. Linear Agent runs in the app and in Slack, writes code through Claude Code and Codex, and ships reviews via Linear Diffs. Skills are reusable saved workflows, Loops run recurring work on a schedule or an event trigger.

About Linear AI

Linear AI is the AI layer inside Linear, the issue tracker and product planning tool used by engineering teams. The headline pieces are Linear Agent (a chat-first agent in the app and in Slack that also writes code through Claude Code and Codex, shipped 11 June 2026), Skills (saved workflows you reuse, included on every plan), Linear Diffs (a native code review tool, shipped 28 May 2026), and Loops (recurring work on a schedule or an event trigger, shipped 20 July 2026, on Business and Enterprise). Sistava operates above the issue tracker layer. Instead of making Linear smarter, Sistava hires an AI Employee that does the work itself, across the tools the job actually needs (sales, marketing, support, ops, engineering coordination). Linear AI wins for engineering teams who already run in Linear. Sistava wins for founders who want a workforce and have not committed to one tracker.

Platform details

Official website

What does Linear AI actually do?

Linear AI is the bundle of AI capabilities inside Linear, the issue tracker and product roadmap tool. Linear Agent is the chat-first interface and runs in the Linear app and in Slack. You can ask it to create issues from a discussion, draft acceptance criteria, summarize a project, or route work to the right human. Skills are saved workflows you reuse across the workspace, and they come with the agent on every plan rather than being gated behind a tier. Since 11 June 2026 the agent also writes code, driving Claude Code and Codex, with Linear Diffs (28 May 2026) giving it a native code review surface. Loops, added 20 July 2026 on Business and Enterprise, run recurring work on a schedule or an event trigger. Linear AI has moved past being a chat layer on a tracker: for engineering work it now picks up a ticket and closes it. Sistava is a different kind of product. The unit is an AI Employee with a role, a team, a schedule, a task board, and a memory. The employee uses Linear when it fits, but also Gmail, Slack, Stripe, the browser, and dozens of other tools. Linear AI improves Linear. Sistava does the job across the stack.

How much does Linear AI actually cost?

Linear's pricing is among the simplest in the category. Free is $0, Basic is $10 per user per month billed yearly, Business is $16 per user per month billed yearly, and Enterprise is custom. Linear Agent and Skills are included on every plan, including Free. Loops require Business or Enterprise. Agent compute is metered on top, and Linear publishes the ranges: roughly $0.07 to $0.20 for a loop run with no coding session, $0.50 to $1 for minor styling work, $3 to $5 for a small bug fix, and $5 and up for something complex. You top up from $10 ad hoc or set a $50 automatic reload. That is honest and legible, and for light use it is very cheap. The shape to watch is the second meter. A team running a few Skills a week barely sees it. A team that wants the agent doing most of the engineering work is buying seats and compute, and the compute line is the one that moves. Sistava keeps compute inside the flat plan, which trades elasticity for a budget you can predict a year ahead.

When does Linear AI beat alternatives?

Linear AI wins for engineering and product teams that already run in Linear. The interface is the cleanest in the category, Linear Agent is sharp at issue-shaped work, and the Slack surface lets humans converse with the agent in the channel they already use. For shipping software, this is a strong default, and now that the agent writes and reviews code it is a strong default for more of the work than it used to be. It also wins for teams that want a calm, opinionated tool. Linear has a reputation for product taste, and the AI features inherit that taste. There is no AI Studio sprawl, no credit dashboard, no pop-up agents. The agent shows up when called and otherwise stays out of the way. Finally, the coding agent and Linear Diffs are exactly what engineering teams want, and they are shipped rather than promised. If your work is mostly code and tickets, Linear is closer to where you live than a general-purpose AI workforce, and we would point you there.

Where does Linear AI fall short outside engineering?

Linear is shaped by engineering and product. The data model is issues, cycles, and projects, and the AI inherits that shape. For sales, marketing, customer support, finance, or ops work, Linear is not the surface, and Linear AI is not the agent. A founder running a full company will hit that ceiling quickly. The second gap is outcome ownership. Linear Agent is great at acting on an issue. It is not designed to own a sales pipeline, a marketing calendar, or a support queue end to end. Sistava ships AI Employees whose whole job is to own those outcomes, including the parts that never become a Linear issue. Cross-tool work outside the engineering stack is the third gap. Linear's integrations and its MCP server cover the issue-adjacent tools well. They do not cover the full surface of a go-to-market or ops job (CRM updates, outbound, billing, partner portals). Honest take: Linear AI is excellent at what Linear is for. For a founder running a whole company through one product, that is a partial win.

How does Linear AI handle pricing as it scales?

Linear keeps the entry cost friendly: Agent and Skills are included on every plan, even Free. That is the right move for adoption and it makes Linear AI cheap to try. Loops sit on Business and Enterprise, and agent compute is metered separately with published rates. Usage-based pricing for AI agent compute is the industry trend across the PM tools in this comparison. It rewards light use and scales with heavy use. A team running a few Skills a week will barely notice. A team running loops all day, especially loops that open coding sessions, will see the bill move. Sistava made a different call. Compute and model calls are inside the flat plan. The trade is less elasticity, but the budget is predictable. For a small team or a solo founder planning a year ahead, predictable beats elastic.

Comparison