AI agents by tool · Product development
Linear AI agent ideas and workflows
Run a Linear AI agent for the recurring coordination work around a product backlog, without handing your agent definition or runtime to a hosted workflow builder.
1 · Ways to connect
Give the agent real Linear capabilities
Choose the smallest connection that can complete the job. MCP, CLI, API, and SDK options provide service access; a skill only teaches the coding agent how to use that access well.
Linear MCP Server ↗
Issue, project, comment, initiative, and milestone workflows
Hosted Streamable HTTP tools for finding, creating, and updating Linear objects; a separate endpoint exposes read tools only.
Use the read-only endpoint for reporting and review agents that do not need to mutate Linear.
- Authentication
- OAuth 2.1, bearer token, or a team-restricted Linear API key
- Setup reference
- Read-only: https://mcp.linear.app/mcp/readonly · Read/write: https://mcp.linear.app/mcp
Linear TypeScript SDK ↗
A typed, project-owned Linear connector
Strongly typed queries and mutations over Linear's public GraphQL schema.
- Authentication
- Personal API key or OAuth 2.0
- Setup reference
- npm install @linear/sdk
Linear GraphQL API ↗
Custom queries, mutations, and non-TypeScript integrations
Read and mutate Linear entities through the same public GraphQL API used by Linear's applications.
- Authentication
- OAuth 2.0 or a permission- and team-restricted API key
Linear Webhooks ↗
Event-driven starts instead of scheduled polling
Signed change events for issues, comments, projects, cycles, documents, and other supported resources.
Verify the signature and timestamp, then invoke the AgentUse HTTP trigger with the validated event.
- Authentication
- Webhook signing secret; admin permission is required to create workspace webhooks
2 · Agent job ideas
What useful work can start from Linear?
Each idea combines Linear with the services needed to finish one recurring outcome. Open any service to explore its other agent jobs; ideas with a complete tutorial appear first.
Feedback-to-backlog review
Full tutorialGroup evidence-backed customer problems, recover product context, show current Linear coverage, and prepare exact issue or priority changes for review.
- Output
- Backlog review artifact
Support triage and escalation review
Full tutorialReconcile each support request with current issue coverage, then prepare a grounded reply and accountable escalation path for review.
- Output
- Support review queue
Production-error impact review
Full tutorialReconcile ranked Sentry errors with existing Linear coverage, then prepare evidence-backed issue drafts without changing the backlog automatically.
- Output
- Error review artifact
Incident action reconciliation
Full tutorialCompare a resolved incident's evidence-backed action proposals with existing engineering work before owners create or change issues.
- Output
- Action coverage
New-issue triage
Find duplicates, missing acceptance criteria, likely team ownership, and the evidence needed before prioritization.
- Output
- Triage proposal
Stale-work review
Surface blocked or inactive issues whose underlying repository or customer context has changed.
- Output
- Action list
Release readiness brief
Combine issue state with repository signals to explain what is ready, risky, or missing.
- Output
- Readiness brief
Sprint-risk monitor
Detect scope, dependency, and implementation signals that put the current cycle at risk.
- Output
- Risk brief
3 · Questions
Linear AI agent FAQ
Can AgentUse create and update Linear issues?
Yes, when connected to a Linear tool that exposes those actions. AgentUse approval gates can keep those mutations under human review.
Can it compare Linear with GitHub?
Yes. A single agent can use both tools to find issues whose implementation state no longer matches the backlog.
Will it replace our existing Linear workflows?
It is better used for judgment-heavy recurring work around those workflows: prioritizing, investigating, drafting, and escalating exceptions.
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