MAC Mobile Agent Control Mobile control plane for coding agents

Vendor-neutral control plane

Mobile control plane for coding agents.

Launch, monitor, and intervene in terminal-native coding agents running on your own machines, with Gemini CLI as the flagship integration and a vendor-neutral adapter model underneath.

Gemini-first depth Vendor-neutral adapters Built for intervention, not just chat
Fleet Snapshot
Online Machines 1 Private supervisor reachability
Running Agents 3 Gemini-first operations
Warnings / Stuck 1 Escalated visibly
Queued Tasks 2 Capacity-aware routing
running
Gemini Local CLI

Summarizing repo changes on workstation-main

warning
Codex Local CLI

No output for 82s, surfaced for review

queued
Hermes via WSL

Waiting for free worker capacity

Product at a Glance

A cleaner operator surface for launch, monitoring, and intervention.

Mobile Agent Control exists because coding agents are no longer a single-terminal activity. Once work spreads across machines and runtimes, you need a control layer that stays fast, honest, and operational.

Dashboard screenshot placeholder Operator Home
Fleet Summary Running Now Machine Health Recent Activity
Launch

Start a new agent on the right machine with a safe workspace and launch profile.

Monitor

Watch state, heartbeat, last output, and machine health in one place.

Intervene

Restart, stop, retry, or prompt agents as soon as the situation changes.

Launch

Start supervised agent processes remotely using approved launch profiles, runtime-aware options, and safe workspace selection.

Monitor

Track active runs through fleet summary, running agents, machine health, logs, and recent activity instead of polling terminals manually.

Intervene

Stop, restart, retry, and send prompts quickly when long-running work stalls, fails, or needs steering.

Why this exists

Vendor CLIs are strong local tools, but they do not give you a unified mobile control surface for multiple machines, long-running jobs, and runtime-neutral operations.

What It Is

A remote control and monitoring layer for coding agents running on your own machines.

Mobile Agent Control sits above machine-local agent runtimes and gives you an operator-grade surface for launch, monitoring, lifecycle control, logs, machine health, and recent activity.

The Android client and responsive web console are both thin control surfaces. The machine-side FastAPI supervisor owns lifecycle state, worker capacity, websocket event streaming, runtime adapter integration, and audit history.

Control Plane Positioning

  • Remote launch and control for terminal-native agent runtimes
  • Vendor-neutral machine, agent, task, and audit model
  • Gemini-first depth without collapsing the architecture into a single vendor

Why It Exists

Coding agents do not stay neatly bound to one terminal on one machine.

Multiple machines

Desktop, laptop, and remote boxes drift apart quickly when agents are running in different places.

Long-running work

Agent runs can last long enough that you need to check status, logs, and health without staying at the desk.

Fast intervention

When something stalls or fails, you need stop, restart, retry, and prompt actions immediately, not after reconnecting manually.

Neutral control layer

Vendor-specific CLIs are strong local tools, but they are not a unified remote fleet operations surface.

Key Capabilities

High-signal operator workflows built around real agent lifecycle control.

Launch agents remotely

Start new agent processes on selected machines using safe launch profiles, approved workspaces, and runtime-aware options.

Monitor running agents live

See state transitions, elapsed runtime, heartbeat, last output, logs, and warning or stuck indicators in near real time.

Intervene fast

Stop, restart, retry, and prompt active agents from mobile or web without dropping into a terminal session first.

View machine health and activity

Track worker capacity, queue depth, resource hints, recent launches, completions, failures, and supervisor trust signals.

Gemini-first, adapter-driven

Gemini CLI is the flagship integration today, but the architecture is intentionally runtime-neutral through CLI adapters and shared supervisor models.

Dashboard Spotlight

An operator console home screen, not a pile of admin panels.

Operator Dashboard Live
Dashboard screenshot placeholder
Total Connected Machines 1
Running Agents 3
Warnings / Stuck 1
Queued Tasks 2

Running Now

running
Gemini Local CLI

workspace: project-repo

warning
Codex Local CLI

last output 82s ago

Machine Health

Heartbeat: live
Capacity: 1 / 2 busy
CPU: if available
Memory: if available

Recent Activity

Launches, completions, failures, restarts, stop requests, and machine transitions appear here.

One surface for remote operations

The dashboard is the first screen because it should answer the operational questions immediately: what is healthy, what is running, what is stalled, and where should the next launch go.

Fleet summary

Understand total machine state, online vs offline reachability, running agents, queue pressure, and recent completions in a glance.

Running agents

See runtime type, machine, workspace, elapsed time, last output, and fast actions where intervention matters most.

Machine health

Use supervisor heartbeat, capacity, and optional CPU or memory visibility to decide where work should go next.

Recent activity and quick actions

Keep launches, failures, restarts, and retries close to the top-level operator experience instead of buried in detail screens.

How It Works

Control UI on top, machine supervisor in the middle, runtime adapters underneath.

Phone / Browser Android + Responsive Web
Control UI Dashboard, launch, monitoring, intervention
Machine-side supervisor FastAPI orchestration + lifecycle state
Runtime adapters Gemini CLI, Codex CLI, Hermes, more
Coding agent CLIs Real local processes, logs, prompts, restarts
Pre-release transport Private connectivity over Tailscale
Future control plane Cloud-routed transport without UI rewrite

Why This Is Different

Built as a control plane, not a wrapper around a single terminal workflow.

Not tied to one vendor

Shared machine, agent, task, audit, and launch-profile models stay neutral while adapters handle runtime-specific behavior.

Not just local terminal UX

The machine supervisor owns lifecycle state, worker capacity, logs, websocket events, and operator-visible audit history.

Not just chat on mobile

The product is centered on launch, monitoring, health, intervention, and routing decisions across agent workloads.

Gemini-first wedge

Gemini CLI is the deepest supported runtime today, but the product is positioned to extend across other terminal-native coding agents cleanly.

Roadmap

Practical evolution from strong MVP to fuller control plane.

Current

Working MVP

Android and web operator surfaces, supervisor lifecycle control, monitoring, adapters, workspaces, MCP visibility, and Gemini-first runtime support.

Next

Monitoring hardening

Stale-data trust signals, reconnect handling, stronger machine health heuristics, and better failure surfacing.

Next

Dashboard polish

Sharper prioritization, richer recent activity, screenshots, and tighter fast-intervention workflows.

Reliability

Persistence and recovery

Stronger state durability, recovery semantics, and operational trust under supervisor restarts or failures.

Surface

Browser-first control plane

Continue strengthening the responsive web console so it stands as a first-class operator entry point.

Future

Cloud-routed architecture

Move from private direct reachability to a brokered control plane without rewriting the product model.

Technical Details

Deeper technical context stays close, but below the main product story.

Architecture

Read the current control-plane layout, runtime adapter model, and transport abstraction.

Open architecture doc

API and setup

Use the repo README for endpoint coverage, launch profiles, local setup, and runtime notes.

Open setup guide

Source and issues

Browse the code, open issues, or track the latest changes in the public repository.

Open repository

Implementation status

Current builds persist recent supervisor state in SQLite while live scheduling and coordination remain in memory, with recovery hardening still on the roadmap.