01
Provider quota fails silently.
When an account hits its limit, the agent stays alive but stops responding. Without dedicated monitoring, the operation only notices when someone opens the session and sees the last output frozen.
Fuad Maestro
Internal case, running at Fuad Digital
The command cabin for teams of AI agents working together.
A visual panel for what your agents are doing right now, who they are talking to, what got stuck, and what to ask them next. One URL instead of five terminal windows.
Navigable operation. Every agent, every session, every task, every conversation, and every provider has its own page, with a health indicator and a button for the next action.
Agents · auth flow vertical slice
5 configured · 4 active · 1 needs attention
⌘K
Provider quota exceeded
refinery is alive but has not responded for 7min: the claude/opus account hit its limit. Restarting the session lets it resume when the quota recovers.
Needs attention · 1
RF
refinery · claude · opus-4
quota exceeded · no response for 7min
Running · 2
MY
mayor · claude · sonnet-4.6
Adding refresh-token rotation middleware
SC
scribe · gemini · 2.5-pro
Updating changelog for release v0.4.2
Healthy, idle · 2
GA
garbo · codex · gpt-5-1
✓ Tests passing, 47/47 in refresh_token.spec.ts
OL
ollama-mini · local · qwen2.5
paused by operator
4 active
12 open tasks
3.2k/min events
$0.34 last hour
Maestro reads everything happening backstage (events, sessions, messages, tasks, flows) and gives back what was always there, only scattered. Every agent, every session, every task, every provider has its own page. Agent-team operation became navigable.
In orbit
Each agent works at its own pace.
The maestro keeps the pulse.
5 active agents
continuous coordination
operation with a pulse
Maestro's vocabulary, on one page.
The nine terms that recur across this case. When they reappear in the text, they get a dotted underline: hover to review.
- bead
- A trackable task. Has owner, state, history, and an ID. The basic unit of work.
- convoy
- A larger package that breaks into several beads so they can run in parallel.
- sling
- A task dispatched to a specific agent, usually coming out of a bead.
- pool
- A named group of agents of the same role picking work off a shared queue.
- crew · polecat
- Pool types. A crew stays on call; a polecat spins up on demand and disappears when it's done.
- rig
- The project scope where an agent operates (a specific repository or global access).
- formula
- A flow's declared recipe: steps, dependencies, variables. Versioned in a file.
- wisp · molecule
- Execution modes. A wisp is throwaway (quick test); a molecule records each step as a bead.
- A message between agents. Has sender, recipient, context, and stays on the record.
01 · Patterns that scale with the fleet
What operating at scale exposes.
These are patterns intrinsic to coordinating multiple agents in parallel, not the weak spots of a specific operation. The more agents, the more expensive each friction becomes, and Maestro was built to take these six off the table.
02
Diagnosis scattered across windows.
To understand why an agent got stuck, you have to find the right session among several terminals, read long logs, and correlate timing with external events. Every diagnosis costs context.
03
Implicit task routing.
Without a named pool, dispatching work depends on remembering who is free and active. Tasks sit idle because the wrong agent picked them up, or because the right one was not configured to pull.
04
Recurring work turns into loose scripts.
Routines that need to run every day (PR review, build check, dependency sweep) become manual cron jobs or rituals that depend on someone remembering. Hard to audit, hard to change.
05
Dependency steps coordinated by hand.
Research → implementation → review in parallel with testing. Without a declared dependency graph, the only way is to dispatch manually when the previous step finishes, and whoever coordinates ends up on call.
06
No single picture of the team.
"What's running right now?" has no single answer. The information lives scattered across sessions, logs, and messages, but pulling it together from memory means opening several windows, and pieces still go missing.
02 · Configure your team
Each agent is a small package you can version.
A name, a markdown prompt that explains the role, and a config file with the agent's preferences: which provider, which model, which permission mode, which project it operates in. Changing behavior means editing text, not code.
- Behavior lives in a text file. Changing what the agent does means editing text, not code. Versioned like any other piece of the repository.
- Controlled scope. Each agent only operates where you said it could. The same operation runs several parallel scopes without mixing one piece of work with another.
- Automatic cycle. Sleeps when idle, wakes when it has to. No agent sits burning quota, no work waits for someone to dispatch it.
Providers
5supported
03 · Define the work
Flows as recipes. Parallel steps by default.
Describe a workflow declaratively: steps with a title, dependencies, validated variables, conditionals, loops, and a post-run check. The engine reads the graph, fires what can run, waits on what has to wait.
Providers
5supported
Versionable recipe. Trigger where it makes sense.
Daily review flow? Cron. A sweep every time a PR merges? Event trigger. An expensive check that only runs when it's worth it? An external script decides. The engine evaluates everything every 30 seconds and fires automatically.
- Steps with declared dependencies. The engine builds the graph and runs in parallel whatever can.
- Variables with validation. Defaults, required, option lists, regex.
- Four trigger types. Interval, cron, external script check, operation event.
- Two execution modes. wisp, ephemeral, for a quick test; molecule, trackable, when you need to see step by step.
- Post-run auto-validation. A check script decides whether the step passed or retries, up to N times.
04 · Persistent memory
An operation that does not forget.
Work in progress is recorded in real time: an agent crashes, the machine restarts, the session picks up exactly where it stopped. And the team accumulates named memory: rules, context, decisions any agent can consult at any time.
Providers
append-only
Nothing gets lost. The team accumulates.
Every task, message, session, and flow step lives in a continuous real-time history. If an agent crashes, its work waits intact; when it comes back, it picks up exactly where it left off. In parallel, named memory captures rules and context that survive across sessions: any agent can consult, any time.
- Automatic real-time logging. Every change becomes an append-only event. Rebuild any moment of the operation from the log.
- Exact resume after a crash. Agent restarts or machine comes back: the session picks up where it stopped, context preserved, zero rework.
- Named long-term memory. Capture rules, patterns, process decisions. Any agent can consult. Search by name or by content.
- The team does not forget. What you taught last week is still there. Changed? Update the entry. No longer valid? Delete it.
05 · Day-to-day operation
Everything that happens backstage on one timeline.
Agent came online, task opened, message sent, session ended, alert fired, flow advanced a step. Holds ten thousand open events without slowing down. Pause to read at your own pace. A shareable URL so a colleague sees the same slice of time.
Continuous stream. Detail on demand.
One timeline of what's happening right now: agent came online, task opened, message arrived, quota hit. Holds ten thousand open events without slowing down, with filters by type and by agent.
- Universal search A keyboard shortcut opens the bar that does everything: navigate, run an action, find an agent or a task. No commands to memorize.
- Automatic problem detection Stuck agent, quota exceeded, session frozen with a backlog: each pattern comes with a suggestion card and a button to resolve it.
- Attached terminal A side drawer that connects to any agent's session and shows the output in real time, without leaving the panel.
Providers
⌘Ksearch
Fuad Maestro · Fuad Digital case
Built for our own operation. The learning goes to yours.
Maestro is today the internal command cabin at Fuad Digital, where the fleet of agents that ships projects and product runs in parallel, with human supervision at the right points. If you're thinking about using AI to repeat what your team does by hand today, or you already operate several agents and want to organize them, let's talk about what can be adapted to your team.
- 01You tell us what your operation repeats by hand today, or how your agent fleet is organized.
- 02We show Maestro running against Fuad's real operation.
- 03Together we look at where the same logic could fit your team, no commitment.