Multi-agent session monitor and approval console

For

Developers and engineering teams running several AI coding agent sessions at once

Solves

Several AI coding agent sessions run at the same time, and the developer misses stuck sessions, context-limit warnings and pending tool approvals.

Delivers

Reviewed session status board with pending approvals and attention alerts

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce missed approvals and unnoticed stuck sessions while keeping session data on the machine.

1. Watch AI coding agent sessions and show current status.
2. Display a live context-window meter per session.
3. Show live input and output token counts.
4. Report estimated API costs per session.
5. Show file reads, code edits and shell commands as they happen.
6. Alert when a session is stuck, near its context limit or otherwise needs attention.
7. Surface pending tool approval requests for response.
8. Notify when an assistant finishes a task.
9. View and manage multiple active sessions in a list or tabs.
10. Use color-coded badges to identify the active assistant.
11. Keep monitoring in peripheral vision without a full dashboard.
12. Read local data directly so no telemetry leaves the machine.
13. Attach to existing command-line agents without changing how they are launched.
14. Allow monitoring and responding from outside the local network via encrypted relay.
15. Work with tmux to remember session and pane mappings.
16. Offer settings such as filtering by source and toggling token and cost displays.
17. Provide an open source codebase for community contributions and forks.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Local agent session data, context-window usage, token counts and tool-call events

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect local agent session data

4. 3

   Context-window usage

5. 4

   Token counts and tool-call events

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed session status board with pending approvals and attention alerts

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, session mappings, alert rules, approval states, usage allowances, relay permissions, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: watch AI coding agent sessions and show current status; display a live context-window meter per session. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will developers and engineering teams running several AI coding agent sessions at once use it to solve "several AI coding agent sessions run at the same time, and the developer misses stuck sessions, context-limit warnings and pending tool approvals"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Approvals answered within target time and missed attention events per session hour.
4. Measure, then decide. Track approvals answered within target time and missed attention events per session hour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed session status board with pending approvals and attention alerts. Retain the explicit scope boundary: One supported agent command-line interface and one local operating system; final approval and code review remain with the developer.

What the build depends on. Session data upload and preview, asynchronous monitoring jobs, editable alert history, reviewer access and tested export formats. High-fidelity production requires specialist developer QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One supported agent command-line interface and one local operating system; final approval and code review remain with the developer.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined session package. Offer a monthly production allowance after repeat demand. Quote complex remote relay or team rollout separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed session status board with pending approvals and attention alerts. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce missed approvals and unnoticed stuck sessions while keeping session data on the machine. Demonstrate a concrete reviewed session status board with pending approvals and attention alerts using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Developers and engineering teams running several AI coding agent sessions at once professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed session status board with pending approvals and attention alerts from a small authorized input set, with a transparent calculation of approvals answered within target time and missed attention events per session hour and no promised savings.

The first 30 days

1. Week 1: interview five developers and engineering teams running several AI coding agent sessions at once and inspect a recent example of several AI coding agent sessions run at the same time, and the developer misses stuck sessions, context-limit warnings and pending tool approvals.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure approvals answered within target time and missed attention events per session hour, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Approvals answered within target time and missed attention events per session hour. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Approvals answered within target time and missed attention events per session hour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed session status board with pending approvals and attention alerts. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved alert rules, session mappings and review examples, together with reliable delivery for a narrow developer niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for developers and engineering teams running several AI coding agent sessions at once. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

tablo, AgentNotch, Conan and Port22. Compare this product with the buyer's present method on approvals answered within target time and missed attention events per session hour. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Session polling, relay bandwidth, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed session status board with pending approvals and attention alerts. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.