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Show HN: Mission Control – Open-source task management for AI agents
by meisnerd
I've been delegating work to Claude Code for the past few months, and it's been genuinely transformative—but managing multiple agents doing different things became chaos. No tool existed for this workflow, so I built one. The Problem
When you're working with AI agents (Claude Code, Cursor, Windsurf), you end up in a weird situation: - You have tasks scattered across your head, Slack, email, and the CLI - Agents need clear work items, context, and role-specific instructions - You have no visibility into what agents are actually doing - Failed tasks just... disappear. No retry, no notification - Each agent context-switches constantly because you're hand-feeding them work
I was manually shepherding agents, copying task descriptions, restarting failed sessions, and losing track of what needed done next. It felt like hiring expensive contractors but managing them like a disorganized chaos experiment.
The Solution
Mission Control is a task management app purpose-built for delegating work to AI agents. It's got the expected stuff (Eisenhower matrix, kanban board, goal hierarchy) but built from the assumption that your collaborators are Claude, not humans.
The killer feature is the autonomous daemon. It runs in the background, polls your task queue, spawns Claude Code sessions automatically, handles retries, manages concurrency, and respects your cron-scheduled work. One click: your entire work queue activates.
The Architecture
- Local-first: Everything lives in JSON files. No database, no cloud dependency, no vendor lock-in. - Token-optimized API: The task/decision payloads are ~50 tokens vs ~5,400 unfiltered. Matters when you're spawning agents repeatedly. - Rock-solid concurrency: Zod validation + async-mutex locking prevents corruption under concurrent writes. - 193 automated tests: This thing has to be reliable. It's doing unattended work.
The app is Next.js 15 with 5 built-in agent roles (researcher, developer, marketer, business-analyst, plus you). You define reusable skills as markdown that get injected into agent prompts. Agents report back through an inbox + decisions queue.
Why Release This?
A few people have asked for access, and I think it's genuinely useful for anyone delegating to AI. It's MIT licensed, open source, and actively maintained.
What's Next
- Human collaboration (sharing tasks with real team members) - Integrations with GitHub issues and email inboxes - Better observability dashboard for daemon execution - Custom agent templates (currently hardcoded roles)
If you're doing something similar—delegating serious work to AI—check it out and let me know what's broken.
Can this take vague ideas, do iterative design with me, and breakdown tasks to then pass off to agents to build?
I was playing with a very similar project recently that was more focused on a high level input ("Build a new whatever dashboard, <more braindump>") and went back and forth with an agent to clarify and refine. Then broke down into Epics/Stories/Tasks, and then handed those off automatically to build.
The workflow then is iterating on those high level requests. Heavily inspired by the dark factory posts that have been making the rounds recently.
From a glance, it seems like this is designed so that I write all the tasks myself? Does it have any sort of coordination layer to manage git, or otherwise keep agents from stepping on each other?
I've been working on a similar project https://github.com/BumpyClock/tasque . Tracks tasks (Epics, tasks, subtasks) with deps between them. So I plan for an hour or so and then when I walk away from my desk I had the tasks for the agents to code and then I can come back and verify.
Edit: minor note, one additional thing that is in the skill that the tool installs is to direct the agent to create follow up tasks for any bugs or refactor opportunities that it encounters. I find this let's the agent scratch that itch of they see something but instead of getting sidetracked and doing that thing, they create a follow up tasks that I can review later and they can move on.
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Could you tell us what makes this different from other agent orchestration software?
Also I’m struggling to understand the significance of the 193 tests. Are these to validate the output of the agents?
If they’re just there to prevent regressions in your code, the size of a test suite is not usually a selling point. In particular, for a product this complicated, 193 is a small number of tests, which either means each test does a lot (probably too much) or you’re lacking coverage. Either way I wouldn’t advertise “193 tests”.
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Congrats! Great try!
I have a different view point on what to automate and I'm working differently with agents, but I much prefer seeing projects like this on HN to just product announcements.
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Interesting that most of it is markdown
well except the mission control folder
code is mix of old and new style JS eg. function vs. =>
at a cursory glance the UI has way too many buttons/features but probably makes sense when you're in the weeds/using it, it makes sense the more I look at it though
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Crafted by Rajat
Source Code