WorkerKitJoin the waitlistAI workers for Engineering
AI workers for engineering teams that triage issues, summarize pull requests, and keep the sprint board honest without pulling a developer off code. Connect GitHub, Jira, and Linear, then set the firewall rules for what the worker may change.
- Daily GitHub Repo Briefing Language model Reads the last 24 hours of GitHub activity and reports what actually changed: what shipped, what is blocked, what is at risk, and who needs a decision today.
- Weekly Ops Reporter Language model Compiles a weekly status report from your task boards, chat, and GitHub: shipped, moving, blocked.
- Engineering Pulse Language model A morning digest of pull requests needing review, stale branches, failing workflows, and hot issues.
- Slack Team Update Brief Language model Turn relevant Slack activity into a focused team update on progress, blockers, risks, dependencies, changes, and attention needed.
- Release Note Classifier Decision model Returns release-note sections and merge judgments for pull requests so an agent can assemble a changelog.
- GitHub Issue Triage Decision model Reads a repository's open issues and decides what each one is, how badly it hurts, and whether it needs a maintainer.
- Spec to Linear Issues Language model Turns a spec you paste into the run message into Linear issues, one per deliverable, written to your team's standard and never duplicated.
- Release Change Evidence Review Language model Explains the actual changes and unresolved checks for a named release candidate.
- Release Acceptance Review Language model Checks release scope against recorded acceptance and QA evidence.
- Release Readiness Workflow Language model Combines code changes and acceptance evidence into a reviewable release readiness packet.
What is a Engineering AI worker?
An Engineering AI worker is an AI agent set up to run one engineering job unattended, such as triaging incoming issues, summarizing open pull requests, or reconciling a sprint board. It typically touches GitHub pull requests and issues, Jira tickets, Linear issues, and Slack channels, and only the repositories, projects, and fields you approve.
What these workers do
Summarize open pull requests
List GitHub pull requests waiting for review, note which have failing checks or no reviewer, and post the digest to a Slack channel each morning.
Triage new GitHub issues
Read incoming issues, label them by area and severity, and link duplicates so maintainers only see one clean thread per bug.
Reconcile the sprint board
Compare Jira tickets against linked GitHub branches and flag tickets marked done that still have an open pull request.
Chase stale Linear issues
Find Linear issues with no update in a week and nudge the assignee in Slack so nothing quietly falls off the sprint.
How a Engineering AI worker runs
What an engineering worker connects to
An engineering worker connects to the tools your team ships from. Point it at GitHub for pull requests and issues, Jira or Linear for tickets, and Slack for standup digests. You approve which repositories and projects it can read, so a triage worker for one service never reaches another team's private repo.
When it runs
- On a schedule for a morning pull request digest or a sprint reconciliation.
- On a trigger when an issue is opened or a pull request is marked ready.
- On demand when a lead wants the state of review before standup.
What stays under your control
The firewall controls exactly what the worker can touch. You can keep it read-only so it labels and summarizes without merging, limit writes to specific issue fields, and redact anything sensitive in a thread. Per-repository scope keeps it inside the projects you name, rate limits keep Slack calm, and the audit trail records every issue and pull request it opened. Access can be narrowed or revoked at any time.
Engineering questions, answered.
Can an engineering AI worker merge pull requests?
Not unless you explicitly allow it, and most teams do not. An engineering worker typically stays read-only, summarizing and labeling while a developer decides on merges. When you do grant write access, the firewall confines it to the fields and repositories you approve, and every action is logged so nothing ships without a person deciding it should.
Which repositories can it see?
Only the ones you scope in. An engineering worker reaches the GitHub repositories you approve and nothing else, so a worker built for one service cannot read another team's private code. You can add or remove repositories as projects change, redact sensitive content in issues, and the audit trail records every repository it opened.
Can it triage issues across GitHub and Jira?
Yes, an engineering worker can read new GitHub issues, label them, and cross-check them against Jira or Linear tickets you have scoped in. It can flag mismatches or duplicates for a maintainer. The firewall applies your read and write rules to each tool separately, and you can keep it read-only on one while allowing writes on another.
Will it expose secrets from our code or issues?
The firewall reduces that risk by redacting sensitive strings before they reach the model and running DLP scanning over what the worker reads. You scope it to named repositories and can keep it read-only. Content outside its scope stays invisible, every read is logged, and you can revoke a repository's access the moment you have any concern.