The key point is allowing your agents to be fed work from the systems your team already has set up. You set up an agent with whatever skills, plugins, and MCP tools it will need to perform the work, and then launch it via a schedule, webhook, or API call. No one has to open a terminal window and kick off every one of those jobs by hand.

I believe we are transitioning from local copilots to remote agents. Working with AI on a laptop is helpful, but there are also tasks you should be able to delegate and check back on when there's something to look at.

Take a new request in Jira. You could attach it to an agent that interprets the request and generates a system design, then have your workflow pass the result to coding and testing agents. The team determines where human review is necessary. In initial trials, you might check every step of the way. If the team gains confidence in the results, it could let the stages run and check the changes before deploying.

That's exactly the kind of workflow I want to make possible with AstraBox. It manages the execution of agents in sandboxes, streams their output and tracks the session history. Schedules and webhooks make that execution work with whatever else you've got.

It supports Claude Code, Codex, pi, and DeepSeek Harness, with Hermes for personal assistant workspaces. It can be run via Docker Compose or Kubernetes. The code is Apache-2.0 licensed. The README has setup instructions and a demo you can try in your browser.

Comments URL: https://news.ycombinator.com/item?id=49870517

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