Gate – AI workers handle dev tickets in a visual workspace
Sandboxed AI agents coordinate code tasks locally without sending data to cloud.

Ticket-triggered AI engineer when Cursor, Devin, and Sweep already own this category.
Engineering teams using Jira, Linear, or similar ticket tracking systems
Cursor · Devin · Sweep
1. Investigates the ticket across all your repos, and creates a design doc 2. Opens one or more PRs implementing the requested change 3. Responds to comments on its own PRs or on the original ticket. Rebases, addresses feedback, pushes updates
It's not a chat window you have to prompt. It just watches your ticket tracker and starts work as soon as a ticket is created. Nothing gets merged or deployed until one of your engineers has reviewed and signed-off on it.
How it works: Each job spins up an ephemeral docker container in AWS with the Claude Agent SDK. The agent runs a workflow that identifies the relevant repositories, investigates the existing codebase, comes up with a design plan for the ticket's requirements, implements it, and spins up fresh agents to review its own work. Your code and your tickets are only ever made accessible to agents that are working for you, in a fresh docker container in a private subnet.
What makes it different from somewhat-similar products: - completely hosted by us. zero infra or setup needed on your end - no model lock-in. we use whatever is SOTA at the present time - works with whatever ticketing system or version-control system your team is using. no migration needed - watches for comments on its PRs or tickets, and iterates based on your feedback - uses industry best practices for getting optimal ai results. Eg, research, plan, implement, independent review, independent revision, etc - zero manual steps needed to kick off the agent, thus massively accelerating organizational adoption
We built this tool because we got tired of tickets sitting in the backlog for weeks and weeks. Having an AI agent automatically produce a design doc and PR has been a huge accelerator. Even when its output isn’t perfect, it still helps immensely in overcoming inertia - allowing tickets to get resolved in days, instead of weeks.
If this sounds interesting to you, reach out to us and we’d love to give you a demo and answer any questions you may have. We’re happy to build support for any ticketing system and version-control system your team is using.
Sandboxed AI agents coordinate code tasks locally without sending data to cloud.
Jira-first AI workflow when Cursor and Devin start from chat prompts instead.
Full-lifecycle AI dev at $2.5k/mo, but context persistence and code quality TBD post-trial.
Notion-to-PR pipeline with feasibility scoring before code generation, runs entirely local.
Ticket-to-PR automation, but Cursor, GitHub Copilot Workspace, and Devin already own this space.
Tags @promptless on PRs; it drafts docs without leaving GitHub or signing up.