Linear Alternative for AI Agents: Cognibl vs Linear

Linear, which its security page says is trusted by more than 40,000 product teams, now treats AI agents as members of the workspace. Its agents page lists coding agents such as Cursor, OpenAI Codex and Devin alongside Linear's own agent, and once an admin has installed an agent, a team can hand it an issue from the same assignee menu it uses for a colleague. For an engineering lead whose agents are starting to ship real work, the question is no longer whether agents belong on the board. It is what the board should require before agent work counts as finished.

That question matters because trust in AI output is still low among developers. In the Stack Overflow Developer Survey 2025, more developers said they actively distrust the accuracy of AI tools (46%) than said they trust it (33%). In our view, a board that marks agent work as done should carry something a sceptical reviewer can check, and the two tools answer that need in different places.

This post compares one specific mechanism in each product: how an agent's issue reaches Done in Linear, and how a task reaches done in Cognibl, from DILR.AI. It reads both from their own live pages as of 22 October 2026. It touches merge gates only as far as Linear's Done automation depends on them; how to set up required reviews and branch protection is covered in our AI coding agent review queue guide owns, nor the general case for proof of done, which the AI agent work management pillar sets out. For the ClickUp version of this comparison, see Cognibl vs ClickUp Super Agents.

This guide is shipped by the team behind Cognibl, work management for teams where people and AI agents share one board. Or see DATS, our five-stage AI consulting system, if the process needs designing before the tool.

How does Linear handle an issue delegated to an AI agent?

Linear treats an AI agent as a delegate, not an owner. When a team hands an issue to an agent, the human stays the assignee and the agent is recorded as the delegate who works on their behalf. Linear's own page frames this as delegating issues but not accountability. The agent can be mentioned, comment, join projects and open pull requests, while the issue remains in the assigned person's own queue throughout.

The wording on Linear's documentation is direct. In the Assign and delegate issues guide, Linear says: "The assignee remains responsible for the work, while the agent contributes on their behalf." The same page notes that issues are assigned to a single person at a time, that delegated issues still appear in that person's My Issues view, and that the assignment and delegation history is tracked in the issue's Activity feed. Linear presents this as a deliberate design choice, and it is a sound one: accountability stays with a named person.

Under the hood, the Linear developer documentation describes the same model at API level. Assigning an issue to an agent app sets it as the delegate rather than the assignee, and an agent has to request two scopes, app:assignable and app:mentionable, before it can be delegated work or mentioned. Workspace admins install agents and choose which teams each one can reach, and Linear's AI Agents docs state that agents cannot sign in to the app, access admin functionality or manage users. A team that wants to know how these permissions should map onto its own roles is asking an AI operating model question, not a tooling one.

One caveat applies to everything in this section that comes from the developer docs. Linear labels its agent APIs a Developer Preview and says functionality may change before general availability. The details below are as documented on 22 October 2026.

What does Linear show while an agent is working?

Linear shows an agent's progress through an agent session, a record that tracks one agent run on an issue. As documented on 22 October 2026, a session moves through six states: pending, active, error, awaiting input, complete and stale. Linear sets the state automatically from the agent's last reported activity, so a reviewer opening the issue sees whether the agent is working, waiting or finished.

The Developing the Agent Interaction page describes what an agent may report into that session. There are five activity types, validated server side: a thought, an elicitation that asks the user for clarification, an action that describes a tool call and optionally its result, a response that marks work as complete, and an error. An agent is expected to acknowledge a new session within 10 seconds, or Linear marks it unresponsive, and it can attach the URL of the GitHub pull request it opened to the session.

This is a well-designed visibility layer. A person can watch an agent think, see which tools it called and read its final response without leaving the issue. Linear's documentation describes the session as a way to track an agent run; it does not describe it as a completion test. A response activity is the agent reporting that work is complete, written by the agent. Our guide to AI agent task verification explains why an agent's own account of its work is the weakest evidence on the board, however clearly it is presented.

When does a Linear issue move to Done for agent work?

For code work, Linear's GitHub integration can move a linked issue to Done when the pull request merges. By default, Linear moves a linked issue to In Progress when a pull request opens and to Done when that pull request merges. A commit with a closing keyword moves the issue to Done when it reaches the default branch. Each team can change these mappings, and the status can still be set directly in Linear.

The detail is in Linear's GitHub integration documentation. Status automation is configured per team under Workflows and automations, and it can fire when a pull request is drafted, opened, has a review requested, is ready for merge or merges. Closing words such as fixes, closes or resolves in a pull request description tell Linear to apply the merge status when the code lands; non-closing words link the pull request without closing the issue.

This matters for a fair comparison. For code work, Linear's Done is not a bare dropdown: by default it is tied to a merge event. A team that also requires reviews before merging in GitHub already has a meaningful gate for its coding agents, because the merge then follows a review. Our review queue guide, linked above, covers how to set that gate up well, and it is the right first step for most software teams.

What a merge does not prove is that the issue's intent was met. A merged pull request shows that code changed and someone approved it; it does not show that the bug the issue described is gone, that the screenshot matches the acceptance criteria, or that non-code work such as a data reconciliation or a weekly report was done at all. Work that never produces a pull request has no merge event to hang Done on, so its status is set directly, by a person or by an agent with the right scopes, with no evidence requirement documented on Linear's pages.

How does Cognibl decide that agent work is done?

Cognibl, from DILR.AI, decides done with a proof version rather than an event. A task reaches a done status only once proof is attached: a CSV describing the run, with the artefact, screenshots and hashes referenced from inside it. Without a proof version the database refuses the status move, and the same rule applies to a person and to an agent.

The Cognibl product page states the principle plainly: work is finished when it is proved, not when it is claimed. The definition of done, the evidence behind it and every tool call are attached to the task itself, so a reviewer reads the claim and the evidence in one place. Cognibl's Harness template also expects a coverage report alongside the CSV, for work where tests are part of the claim. Two AI flows, proof validation and project status, read that evidence and summarise or flag what does not match, but they never decide; a person keeps the decision.

For coding work specifically, cognibl.com describes agents that run on Claude Code and Codex, open a branch and a pull request for each task, and attach proof of work, with only a person able to merge. So a human merge stays in the loop for code. The difference is that the proof requirement sits on the task, not on the repository; cognibl.com's own walkthrough shows an agent filing proof for a supplier invoice reconciliation, work that never touches a repository.

Cognibl ships four process templates, Harness, Graph, Loop and Custom, that set which status moves are legal in a project. The Harness template, for example, expects build, test and prove before done. Our what is Cognibl explainer walks through each template, and our DATS consulting system is where a team can work out which template fits which flow before anyone opens the tool.

Where does the record of agent work live in each tool?

In Linear, the record of agent work lives on the issue: its Activity feed, the agent session with its thoughts, actions and response, and any linked pull request. In Cognibl, it lives in the task's proof versions and in a project trace written by the gateway. Cognibl's records are append-only and hash-chained, so earlier entries are kept rather than overwritten.

Two clarifications keep this comparison honest. First, Linear's audit log is not where agent task history is kept. It records workspace events such as account access, subscriptions and settings changes, it is available on the Enterprise plan to workspace owners, and entries are retained for 90 days, with streaming to a webhook or SIEM. Agent work history sits on the issue and the agent's user page instead. Second, a third-party agent in Linear is an integration the workspace approved; Linear's docs say it cannot speak to how the agent provider operates, so a buyer should ask each agent provider what it records on its own side.

On the Cognibl side, the product page describes agents reaching a versioned skills and agents library over the Model Context Protocol, through a gateway that is deny by default. Every call is traced, every write is attributed by key name, and the instruction an agent ran in March can still be read in September because library items archive rather than delete. Our MCP skills library governance guide covers that library in depth.

Teams that want this record kept for them as a standing discipline can look at our AI execution office, which is embedded long-term delivery alongside the client team.

What does developer data say about trusting agent output?

Developer data says agent use is real but uneven, and that trust in AI output is low. In the Stack Overflow Developer Survey 2025, 14.1% of respondents said they use AI agents at work daily, while 37.9% said they do not use agents and do not plan to. The same survey found more developers distrust the accuracy of AI tools than trust it, which in our view strengthens the case for checkable evidence.

The full distribution from the Stack Overflow 2025 AI section shows why a board should not assume every reviewer accepts an agent's word. Fewer than a third of respondents use agents at work at any frequency, and the largest single group has no plans to. The same survey reports that 84% of respondents are using or planning to use AI tools in development, so interest in AI is broad even where agent use is not. Our AI developer productivity page sets out what AI coding assistance does and does not do to throughput, using primary research.

For an engineering lead, the practical reading is that a board will hold a mix of people who trust agent output and people who do not. A done status that carries its own evidence serves both: the sceptic can open the proof, and the convinced reader loses nothing. That is the gap the proof of done pillar argues for in general terms, and the comparison above shows where each tool places it.

When is Linear the better choice for a team running agents?

Linear is the better choice when agents mainly write code, every change lands through a reviewed pull request, and the team already lives in Linear for planning. Its merge-triggered Done, agent sessions and wide agent directory serve that case well. It also publishes SOC 2 Type II and ISO/IEC 27001:2022 credentials, which Cognibl's live pages do not show, and that matters to a security reviewer.

Linear's security page also lists HIPAA support with a business associate agreement, a choice of EU or US data hosting at workspace creation, and app approvals that let admins control which third-party applications reach the workspace. A team in procurement with a hard certification requirement should weigh that heavily. Cognibl's live pages carry no certification claim, and a buyer should not assume one.

Linear also wins on breadth of agents. Its directory includes coding agents from Cursor, OpenAI Codex, Devin and Factory, plus Sentry for root-cause analysis and ChatPRD for requirements. Cognibl's own coding agents run on Claude Code and Codex. A team that wants to try many agent vendors against the same backlog will find more of them already wired into Linear.

If those conditions describe your team, stay on Linear and invest in the merge gate. The evaluation harness engineering write-up shows how to make that gate test behaviour rather than just syntax, and our AI placement diagnostic exists to settle where AI belongs before a second tool is bought.

When does a team outgrow merge-triggered Done?

A team outgrows merge-triggered Done when agents start doing work that does not end in a pull request, or when people outside engineering must trust that work. Reconciliations, reports, recurring checks and content tasks have no merge event, so their status is set directly, without the evidence a merge implies. At that point a rule that asks for proof on every task, code or not, closes the gap.

Signals to watch for: an operations or finance owner starts asking engineering whether an agent's weekly job really ran. A recurring task needs evidence every time, not only the first time. A reviewer wants the screenshot or output file attached to the task rather than linked from a chat thread. Cognibl's recurring tasks, as cognibl.com describes them, treat each scheduled run as a new task with its own proof, which is the pattern these signals point to.

Cognibl also reports on delivery from verifications rather than raw task counts. Its five delivery metrics are median cycle time split across spec, build, verify and settle, first-pass verification rate by flow type, human wait share, verified throughput per week, and reopen rate within 30 days. Reopen rate is the one that tells a manager whether done meant done. Our LangGraph work management post shows the same done-gate sitting on top of an agent framework rather than a tracker.

When the change is organisational rather than technical, the tool is the smaller decision. Our DATS methodology is a five-stage system that begins with discovery and diagnosis, which is where the question of which flows deserve a proof gate belongs, and the approach page sets out how we place AI inside existing systems rather than beside them.

What is the best Linear alternative for AI agents in 2026?

The best Linear alternative for AI agents depends on what done must prove. For code-only teams with reviewed merges, there may be no need for an alternative, and Linear remains a strong choice. For teams where agents produce non-code work, or where outsiders must trust completions, Cognibl's proof-gated done is the closer fit. Jira, Asana and ClickUp deserve the same done-status test before any switch.

Scored against four criteria, the verdict is narrower than a headline. On agent breadth and published certifications, Linear wins today, and we say so plainly. On evidence attached to every completion, regardless of work type, Cognibl wins, because its done status is refused without a proof version. On keeping people in the decision, each keeps a person involved in a different place: Linear keeps the human as assignee of a delegated issue, while Cognibl keeps a person on the merge and on every decision its AI flows only flag.

There is also a third path, and it is the one we would usually suggest trying first. Keep Linear for planning, turn on required reviews and status automation, and pilot a proof gate on one non-code flow where trust is currently weakest. If that flow improves on reopen rate and reviewer time, extend it. If it does not, you have lost little. If you want delivery help with the pilot, our AI execution office is embedded long-term delivery, and the ClickUp comparison linked above applies the same criteria to a different tracker.

Frequently asked questions

Can Cognibl and Linear run side by side?

Cognibl and Linear can run side by side, with limits a team should plan for. Cognibl's live page names Linear among the tools its agents can read, with read access granted per project and read-only by default. Nothing on either product's pages describes a status sync, so a Cognibl proof gate does not gate a Linear issue's Done status.

A workable pattern is to keep planning and the roadmap in Linear, move the flows that need proof onto a Cognibl board, and agree who updates which status. Our operating model service covers governance, RACI and lifecycle, which is where that handoff and its owners get written down.

Does Cognibl hold SOC 2 or ISO 27001 certification?

Cognibl's live pages do not show any certification, including SOC 2, ISO 27001 or ISO 42001, so a buyer should treat that as unverified. Linear's security page shows SOC 2 Type II audits and ISO/IEC 27001:2022 certification. If certification is a hard procurement requirement, Linear currently meets it on published evidence and Cognibl does not.

Cognibl does describe its own controls in detail: a deny-by-default MCP gateway, proof versions attached to each task and an append-only, hash-chained trace. Those are product design facts, not third-party attestations. You can ask us about them directly through the contact page.

Are Linear's agent APIs stable enough to build on?

Linear describes its agent APIs as a Developer Preview and says functionality may change before general availability. Teams building their own Linear agent should expect the session model, activity types and scopes documented in October 2026 to evolve. Agents already listed in Linear's directory are maintained by their own vendors, which carry that change risk.

Before building on either platform, it is worth writing down what evidence a completed agent task must carry, independent of the tool. That definition outlives any API. If you want an outside view on it, start with our AI placement diagnostic.

Want to see a proof-gated board in practice? Try Cognibl live, read the Cognibl explainer, see measured AI developer productivity, or browse more AI strategy articles from the Dilr.ai team.

Written by the Dilr.ai engineering team, practitioners who ship enterprise AI in production. Follow us on LinkedIn for shipping notes, or subscribe via the RSS feed.