Building Maestro: What I Learned Automating My Mornings with AI

When I started building Maestro, I thought the hard part would be the AI orchestration. It wasn’t. The hard part was deciding what the system should actually do with all the data it collected.

The Problem

Every morning I’d wake up and spend 20-30 minutes doing the same mental overhead: checking the weather, glancing at my calendar, reviewing what I’d left unfinished, figuring out if I had time for a run. None of this required intelligence — it just required aggregation. I wanted to outsource that to a machine.

The Architecture

Maestro runs as a multi-agent DAG (Directed Acyclic Graph). Each agent owns a single data source:

• WeatherAgent — pulls tomorrow’s forecast and flags anything that affects plans (rain during your commute window, heat index during a planned run)
• CalendarAgent — reads your Google Calendar, identifies hard commitments, and estimates travel/prep time
• FitnessAgent — checks Strava for your training load and suggests whether tomorrow is a recovery day or a push day
• PriorityAgent — reads a simple markdown file where you jot ongoing priorities and incomplete tasks
• SynthesisAgent — takes all outputs and produces a single daily brief, with a concrete schedule suggestion

The agents run in parallel where there are no dependencies, then the SynthesisAgent waits for all of them before running.

What I Got Wrong First

My first version had the SynthesisAgent trying to be too smart. I gave it too much context and asked it to make too many decisions. The output was verbose and hard to scan at 6am.

The fix was to constrain each upstream agent to output structured data (not prose), and limit the SynthesisAgent’s job to formatting, not reasoning. The reasoning happens at the individual agent level. This made the final output tighter and more predictable.

The Unexpected Win

The most useful thing Maestro produces isn’t the schedule — it’s the conflict detection. When your calendar shows a 9am meeting but WeatherAgent flagged a 45-minute commute delay and FitnessAgent says you’re in a recovery week, those conflicts get surfaced explicitly. I didn’t build that as a feature; it emerged from the agents sharing state.

Open Source

Maestro is open source on GitHub. The README walks through setup. It’s opinionated toward my own workflow but designed to be forked and adapted.

If you build something with it — or have questions about the architecture — reach out. I’m always happy to talk multi-agent systems.