Mission Control for Personal AI Agents

Context

This project started as a practical problem: personal AI agents can do useful work, but their activity quickly becomes fragmented across conversations, logs, and background tasks.

What I wanted to build

A working prototype for seeing what is running, what has finished, what is waiting, and where I need to step in—without pretending the agents are more reliable than they are.

System design

Mission Control sits between agent activity and the person operating the system. It collects a small set of task and status signals, then presents them as an operations view rather than another chat interface.

Hardware and software

The system currently runs on repurposed PC and homelab hardware. Services are containerised so individual experiments can change without rebuilding the whole platform.

Build process

I started with the questions I repeatedly asked while running agents: What is active? What changed? Is anything waiting for me? The interface grew from those questions rather than from a generic dashboard template.

Problems encountered

The first approach treated every event as equally important. The result was complete, but hard to scan.

How I solved them

I reduced the visible states, separated current work from history, and made human attention the primary signal.

Final result

The project is an in-progress personal operations layer. It already provides one place to inspect agent work, but the reliability and integrations are still evolving.

What I learned

Observability matters for personal automation too. A system is easier to trust when it shows uncertainty, waiting states, and failures clearly.

What I would improve next

I want to improve event history, add clearer recovery actions, and document the architecture once the interfaces stop moving.