AI app for it and development · no coding needed
Multi-agent session monitor and approval console
Reduce missed approvals and unnoticed stuck sessions while keeping session data on the machine.
Made for: Developers and engineering teams running several AI coding agent sessions at once
What it does for you
The problem
Several AI coding agent sessions run at the same time, and the developer misses stuck sessions, context-limit warnings and pending tool approvals.
What it gives you
Reviewed session status board with pending approvals and attention alerts
What you give it
Local agent session datacontext-window usagetoken countstool-call events
Build your own version of tablo, AgentNotch and more
One app with what these 4 AI tools do, yours to keep and change: tablo, AgentNotch, Conan, Port22.
Everything these tools do, in one app
- Session monitoring Watches AI coding agent sessions and shows their current status.Found in tablo, AgentNotch, Conan and 1 more
- Context-window meter Displays a live meter of how full each session's context window is.Found in tablo, Conan
- Token usage tracking Shows live input and output token counts during sessions.Found in AgentNotch, Conan
- Cost estimation Reports estimated API costs to help avoid surprise bills.Found in AgentNotch, Conan
- Tool call visibility Shows file reads, code edits, shell commands, and other tool calls as they happen.Found in AgentNotch, Conan
- Attention nudges Alerts you when a session is stuck, near its context limit, or otherwise needs attention.Found in tablo, AgentNotch, Port22
- Tool approval requests Surfaces pending tool approval requests so you can respond.Found in tablo, Port22
- Completion notifications Alerts you when an assistant finishes a task.Found in AgentNotch, Port22
- Multi-session management Lets you view and manage multiple active sessions, often in separate tabs or a list.Found in tablo, Conan, Port22
- Source-aware indicators Uses color-coded badges to identify which assistant (e.g., Claude Code or Codex) is active.Found in AgentNotch
- Peripheral glanceable display Keeps monitoring in your peripheral vision without occupying screen space with a full dashboard.Found in tablo, AgentNotch
- Local data privacy Reads local data directly so no telemetry leaves the machine.Found in Conan
- Zero-config attachment Attaches to existing command-line agents without requiring changes to how they are launched.Found in Port22
- Remote connectivity Allows monitoring and responding to sessions from outside the local network via encrypted relay.Found in Port22
- tmux integration Works with tmux to remember session and pane mappings.Found in tablo
- Configurable display options Offers settings such as filtering by source and toggling token/cost displays.Found in AgentNotch
- Open source Provides an open source codebase for community contributions and forks.Found in tablo
How it works, step by step
- Watch AI coding agent sessions and show current status
- Display a live context-window meter per session
- Show live input and output token counts
- Report estimated API costs per session
- Show file reads, code edits and shell commands as they happen
- Alert when a session is stuck, near its context limit or otherwise needs attention
- Surface pending tool approval requests for response
- Notify when an assistant finishes a task
- View and manage multiple active sessions in a list or tabs
- Use color-coded badges to identify the active assistant
- Keep monitoring in peripheral vision without a full dashboard
- Read local data directly so no telemetry leaves the machine
- Attach to existing command-line agents without changing how they are launched
- Allow monitoring and responding from outside the local network via encrypted relay
- Work with tmux to remember session and pane mappings
- Offer settings such as filtering by source and toggling token and cost displays
- Provide an open source codebase for community contributions and forks
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Multi-agent session monitor and approval console with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Multi-agent session monitor and approval console with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data197 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce missed approvals and unnoticed stuck sessions while keeping session data on the machine. For developers and engineering teams running several AI coding agent sessions at once, convert local agent session data, context-window usage, token counts and tool-call events into a reviewed session status board with pending approvals and attention alerts. The benefit is a testable hypothesis, measured through approvals answered within target time and missed attention events per session hour; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect local agent session data, context-window usage, token counts and tool-call events, then follow this sequence: 1. Watch AI coding agent sessions and show current status. 2. Display a live context-window meter per session. 3. Alert when a session is stuck, near its context limit or otherwise needs attention. Resolve uncertain cases with qualified reviewers, approve reviewed session status board with pending approvals and attention alerts, and measure approvals answered within target time and missed attention events per session hour against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One supported agent command-line interface and one local operating system; final approval and code review remain with the developer. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve developer intent, source attribution, command accuracy and usage permissions. Developers approve substantive changes and deployment scope. One supported agent command-line interface and one local operating system; final approval and code review remain with the developer. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One supported agent command-line interface and one local operating system; final approval and code review remain with the developer. Implement one approved input format, a bounded representative case set and the first two task modules: watch AI coding agent sessions and show current status; display a live context-window meter per session. Support the third module with operator review: alert when a session is stuck, near its context limit or otherwise needs attention. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Developer-owned agent command-line sessions, local session logs and permitted terminal sources. Cloud relay endpoints, notification destinations and editor or terminal import/export. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Session list and status board, Session detail with tool-call stream, Approvals and alerts queue. Use a compact list or tab strip for active sessions, a central detail pane for one session, and a right-hand panel for context meter, token counts, cost estimate and pending approvals. Let users compare sessions side by side. Display running, waiting for approval, stuck, near limit and finished states. Provide a glanceable peripheral strip and a remote view link with the same approval actions. Make the task-specific outcome reviewed session status board with pending approvals and attention alerts visible beside its evidence, review state and value baseline.
