How to Build an AI Agent for GitHub and Linear

The workflow problem

Development teams that use Linear for project management and GitHub for source control operate in two systems that should be mirrors of each other but rarely are. Linear tracks what should be built. GitHub tracks what was built. The alignment between intent and implementation is maintained by convention — branch naming patterns, commit message prefixes, manual status updates — and conventions break down under real-world pressures.

The most common failure mode is simple: an engineer finishes a feature, merges the PR, and forgets to move the Linear issue to “Done.” The issue sits in “In Progress” for days until someone notices during a standup. Sprint velocity calculations become inaccurate. Cycle reports show less completed work than was actually shipped, which distorts planning for future cycles.

The reverse problem is equally damaging. A Linear issue is created, prioritized, and assigned, but the engineer starts working on it without creating a properly linked branch. They push code, open a PR, and merge it, but none of this activity is connected to the Linear issue. When a product manager asks “what’s the status of that feature?”, the engineer has to manually map their GitHub activity back to the Linear issue.

Bug reports create additional friction. When a GitHub issue is filed by an external contributor or surfaced by CI, someone needs to create a corresponding Linear issue, triage it, assign it to the right team, and set the priority. This manual step means bugs reported on Friday evening sit untracked in Linear until Monday, even if a developer already pushed a fix over the weekend.

At scale, these disconnections compound into a project management system that leadership cannot trust. If Linear’s data does not accurately reflect what is happening in GitHub, sprint reviews, velocity tracking, and capacity planning all operate on fiction.

Why an AI agent, not just automation

Linear offers a native GitHub integration that can link PRs to issues based on branch naming conventions and move issues when PRs are merged. This covers the happy path but misses the cases that cause the most pain.

An AI agent handles the unhappy paths. When an engineer opens a PR that references issue LIN-456 in the branch name but also modifies code related to LIN-389 (a different issue about the same module), the agent can identify both connections. The native integration would only catch the explicit reference. The agent reads the PR diff, understands what changed, and maps those changes to all relevant Linear issues.

The agent also adds intelligence to issue creation. When a GitHub issue is filed with a vague title like “Authentication broken on mobile,” the agent can check the GitHub issue for additional context (stack traces, reproduction steps, affected versions), search Linear for potentially duplicate issues, and create a properly categorized Linear issue with appropriate priority based on severity signals. A simple automation would create a bare-bones issue with no triage.

Velocity tracking benefits from the agent’s analytical capabilities. Rather than just counting completed issues, the agent can analyze the relationship between issue estimates, actual PR complexity (measured by diff size, files changed, and review time), and cycle completion rates. It can surface patterns like “issues tagged ‘backend’ consistently take 2x their estimates” — an insight that requires correlating data across both platforms.

The agent also handles the ongoing maintenance that teams never get to. It can identify Linear issues that have been in “In Progress” with no associated GitHub activity for a configurable period, or GitHub branches that have been open for weeks without a PR. These hygiene tasks are valuable but tedious, making them perfect for an intelligent agent.

How it works with ACP

The Agentic Control Plane provides your agent with authenticated access to both GitHub and Linear, enabling it to serve as the synchronization layer between your code and project management:

GitHub tools: Read repositories, branches, and commit histories. View pull requests including diffs, review comments, and CI status. Read and create issues. View and comment on PRs. Check GitHub Actions workflow runs. The agent understands the full lifecycle of code changes from branch creation through merge.

Linear tools: Create and update issues with full field control (title, description, priority, estimate, labels, assignee). Manage issue status through workflow transitions. Read project and cycle data. Create and manage labels. Link issues to external URLs. Query issues by any field combination. The agent manages the project tracking layer based on code activity.

Here is how the agent handles several common scenarios:

Scenario 1: PR opened without explicit Linear reference

An engineer opens PR #234 titled “Fix race condition in job scheduler” with no Linear issue reference in the branch name or description.

Agent (comments on the PR): “I found two potentially related Linear issues:

• INFRA-167: ‘Job scheduler occasionally processes duplicate jobs’ (In Backlog, High priority)
• INFRA-201: ‘Intermittent timeout in scheduled task runner’ (In Progress, assigned to @alex)

Based on the diff, this PR modifies the job locking mechanism, which aligns most closely with INFRA-167. Should I link this PR to INFRA-167 and move it to In Progress? Or is this addressing a different issue?”

Engineer: “Yes, link to INFRA-167.”

Agent: “Done. INFRA-167 is now In Progress with PR #234 linked. I have also added a note about the duplicate job processing fix to INFRA-201 in case it is related.”

Scenario 2: GitHub issue filed by external user

A user opens GitHub issue #89: “API returns 500 when requesting user profile with special characters in the name field. Stack trace: NullPointerException at UserSerializer.java:142.”

Agent (creates Linear issue): “Created API-445: ‘API 500 error on user profile with special characters (NullPointerException in UserSerializer)’. Priority: High (production error with stack trace). Team: API. Label: bug. Linked to GitHub issue #89. I checked for duplicates — API-398 (‘Special character handling in user input’) is related but covers input validation, not serialization. I have linked them as related issues.”

Scenario 3: PR merged

PR #234 is merged to main. CI passes. Deployment to staging succeeds.

Agent: “PR #234 merged. INFRA-167 moved to Done. This was estimated at 3 points and took 4 days from branch creation to merge with 2 review cycles. Your current cycle (Sprint 12) is now at 78% completion with 5 days remaining.”

The agent transforms GitHub and Linear from loosely connected tools into a tightly synchronized engineering system.

Governance and security

Source code repositories and project tracking data represent core engineering intellectual property. Access controls must be precise.

GitHub OAuth scopes: The agent uses repo:read for repository access, pull_request:read and pull_request:write (for comments only) for PR interaction, and issues:write for issue management. The agent cannot push code, merge pull requests, delete branches, or modify repository settings. Its interaction with the codebase is read-only at the code level, with write access limited to comments and issue management. Repository-level access is controlled through GitHub’s fine-grained permissions — the agent can only access repositories it has been explicitly granted access to.

Linear OAuth scopes: The agent authenticates with scopes for reading and writing issues, reading projects and cycles, and managing labels and assignments. It operates within the teams and workspaces it has been granted access to. If certain Linear teams are restricted (for example, a security team with sensitive vulnerability tracking), the agent respects those boundaries.

Code exposure controls: When the agent creates Linear issues from GitHub data, it references code at an architectural level (file names, function names, error types) rather than copying source code into Linear. This prevents code from being exposed in a platform that may have different access controls. ACP allows you to configure how much code detail the agent includes in Linear issue descriptions.

Audit trails: Every Linear issue created or modified, every GitHub comment posted, and every status transition is logged in the ACP dashboard with the triggering event (which PR merge, which GitHub issue, which status change) and timestamp. Engineering leadership can verify that the synchronization is accurate and that the agent is not accessing repositories outside its configured scope.

Example use cases

• Automatic status synchronization: When a PR linked to a Linear issue is opened, the issue moves to “In Review.” When CI passes and the PR is approved, it moves to “Ready to Merge.” When merged, it moves to “Done.” No manual status updates needed.

• Issue mirroring: GitHub issues filed by external users or CI systems are automatically created as triaged Linear issues with appropriate priority, team assignment, and labels based on the content and context of the GitHub issue.

• Velocity analytics: The agent tracks the relationship between Linear estimates and actual GitHub implementation effort (PR size, time to merge, review cycles), surfacing patterns that help teams calibrate their estimation accuracy.

• Stale work detection: Issues that have been in “In Progress” in Linear with no corresponding GitHub branch or PR activity for more than a configurable period are flagged, helping teams identify blocked or abandoned work.

• Dependency awareness: When a PR modifies code that is referenced by other open Linear issues, the agent notifies the assignees of those issues that their work may be affected by the upstream change, preventing integration conflicts.

Getting started

1. Sign up at cloud.agenticcontrolplane.com — free accounts include both GitHub and Linear connections.

2. Connect your tools by authenticating GitHub and Linear through OAuth. Select which repositories and Linear teams the agent should synchronize.

3. Describe your workflow in plain English. Tell the agent your branching convention, how you want status transitions mapped, and whether it should auto-create issues or propose them for review. Your GitHub-to-Linear synchronization will be active within minutes.