Cross-device AI coding agent control plane

For

Engineering teams running AI coding agents across several devices and providers

Solves

Agent sessions, contexts and approvals are scattered across tools and devices, so teams cannot see or control what agents do.

Delivers

A source-linked assistant and administrator console with recorded approvals

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Run and manage AI coding agents across devices with shared context and team controls.

1. Assist with writing, completion and debugging.
2. Connect multiple providers and models in one interface.
3. Route tasks to the most suitable model.
4. Create and manage autonomous planning agents.
5. Run parallel long-lived agents with branch and model control.
6. Give each provider agent its own git worktree.
7. Sync sessions and contexts across devices.
8. Monitor, approve and push from a phone.
9. Queue and checkpoint long-running background tasks.
10. Keep a persistent agent VM available.
11. Start agents in pre-configured sandboxes.
12. Capture terminal output from running agents.
13. Index captured text into a local vector store.
14. Let agents query their own and other agents' past turns.
15. Inject custom rules into every message.
16. Centralize team interactions in a shared workspace.
17. Set provider, usage and rule controls for admins.
18. Reuse prompt manifests for repeated tasks.
19. Track and limit provider spending.
20. Connect credentials once and reuse them.
21. Keep API keys on the user device.
22. Gate state-changing commands with approvals.
23. Review and merge pull requests in the app.
24. Add or customize AI tools.
25. Self-host the open-source code.
26. Share agents, progress and results asynchronously.
27. Pool personal devices as private compute with fallback rules.

Everything these tools do, in one app

• AI coding assistance Helps users write, complete, and debug code with AI.Found in PearAI
• Multi-provider agent support Lets users work with multiple AI coding subscriptions or models in one interface.Found in ADE, KarmaBox
• Multi-model routing Automatically sends tasks to the most suitable AI model.Found in KarmaBox
• Agent orchestration Creates and manages autonomous agents that plan and execute development tasks.Found in Google Antigravity
• Parallel agent management Runs multiple long-lived agents in parallel and manages branches and models from one place.Found in Google Antigravity
• Git worktrees for agents Lets agents from different providers work on separate worktrees in the same repository without interfering.Found in ADE
• Cross-device session sync Keeps chat sessions, agent contexts, or terminal sessions synchronized across devices.Found in ADE, scritty
• Mobile monitoring and control Allows users to monitor progress, approve actions, and push changes from a phone.Found in Grass
• Background long-running tasks Queues and checkpoints work so tasks continue when devices go offline and notifies when complete.Found in KarmaBox
• Persistent agent VM Provides an always-ready virtual machine for agent sessions that stays available even if the user's device is offline.Found in Grass
• Pre-configured agent environments Offers ready sandboxes so agents can run without local environment setup.Found in Grass
• Terminal output capture Records conversations from command-line AI coding agents directly from the running process.Found in scritty
• Local vector indexing Indexes captured text into a local searchable vector store, with options for external databases.Found in scritty
• Agent memory querying Allows agents to query their own past turns and the past turns of other agents.Found in scritty
• Custom prompt injection Injects custom rules into every message before it reaches the active agent via a configuration file.Found in scritty
• Shared team workspace Centralizes team AI interactions in a unified chat with shared context.Found in Intrascope.app
• Admin usage controls Lets admins select providers, set usage limits, and enforce rules.Found in Intrascope.app
• Reusable prompt manifests Standardizes prompts and workflows for repeated tasks.Found in Intrascope.app
• Cost management Provides controls to choose providers and manage spending.Found in Intrascope.app, KarmaBox
• Unified credentials Connects models and apps once and reuses them across agents.Found in KarmaBox
• BYOK security Keeps API keys on the user device rather than storing them on servers.Found in Grass
• Execution approvals Gates commands that change state with contextual approvals and scoped auto-approvals for lower-risk actions.Found in Grass, Google Antigravity
• Built-in PR management Reviews and merges pull requests without leaving the application.Found in ADE
• Extendable architecture Allows users to add or customize AI tools.Found in PearAI
• Open source self-hosting Provides open-source code and the option to run on own infrastructure.Found in ADE, PearAI
• Async collaboration Shares agents, progress, and results with teammates without synchronous sessions.Found in Google Antigravity
• Device compute pooling Turns personal devices into a coordinated private compute pool with fallback rules.Found in KarmaBox

What goes in, what comes out

The workflow

1. In
   Start with

   Provider subscriptions, repository access, device sessions and team rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect provider subscriptions

4. 3

   Repository access

5. 4

   Device sessions and team rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   A source-linked assistant and administrator console with recorded approvals

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 routing rules, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository set and approved provider list; final code review and merge decisions remain with engineers. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent console, Shared team workspace, Admin controls. Use a device and agent list for sessions, a large central task view with terminal and diff output, and a right-hand panel for context, approvals and cost. Let users compare agent branches side by side. Display running, waiting for approval, blocked and merged states. Provide a mobile view for monitoring, approvals and pushes. Make the task-specific outcome a source-linked assistant and administrator console visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, provider connections, credential locations, agent versions, approval states, usage limits, cost caps, device pool rules, 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

Team-owned repositories, authorized provider accounts and permitted device sessions. Cloud or self-hosted storage, git hosting, CI and issue trackers. Start with file exchange and validate destination specifications before promising direct deployment. 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: assist with writing, completion and debugging; connect multiple providers and models in one interface. 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 engineering teams running AI coding agents across several devices and providers use it to solve "agent sessions, contexts and approvals are scattered across tools and devices, so teams cannot see or control what agents do"?
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: Accepted agent tasks per engineering hour and unapproved state-changing actions.
4. Measure, then decide. Track accepted agent tasks per engineering hour and unapproved state-changing actions; 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 repository set and approved provider list; final code review and merge decisions remain with engineers. Implement one approved input format, a bounded representative case set and the first two task modules: assist with writing, completion and debugging; connect multiple providers and models in one interface. Support the third module with operator review: route tasks to the most suitable model. 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 a source-linked assistant and administrator console with recorded approvals. Retain the explicit scope boundary: One repository set and approved provider list; final code review and merge decisions remain with engineers.

What the build depends on. Repository upload and preview, asynchronous agent jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One repository set and approved provider list; final code review and merge decisions remain with engineers.

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 repository package. Offer a monthly production allowance after repeat demand. Quote complex multi-team or self-hosted deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked assistant and administrator console with recorded approvals. 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

Run and manage AI coding agents across devices with shared context and team controls. Demonstrate a concrete source-linked assistant and administrator console with recorded approvals using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams running AI coding agents across several devices and providers professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample source-linked assistant and administrator console with recorded approvals from a small authorized input set, with a transparent calculation of accepted agent tasks per engineering hour and unapproved state-changing actions and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams running AI coding agents across several devices and providers and inspect a recent example of agent sessions, contexts and approvals scattered across tools and devices.
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 accepted agent tasks per engineering hour and unapproved state-changing actions, 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: Accepted agent tasks per engineering hour and unapproved state-changing actions. 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

Accepted agent tasks per engineering hour and unapproved state-changing actions; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a source-linked assistant and administrator console with recorded approvals. 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 agent configurations, routing rules and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering teams running AI coding agents across several devices and providers. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

ADE, PearAI, Google Antigravity, scritty, KarmaBox, Grass and Intrascope.app, plus separate provider subscriptions and local scripts. Compare this product with the buyer's present method on accepted agent tasks per engineering hour and unapproved state-changing actions. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Provider usage, agent VM and sandbox compute, 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 a source-linked assistant and administrator console with recorded approvals. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve code ownership, source attribution, license compliance and usage permissions. Engineers approve substantive changes and deployment scope. One repository set and approved provider list; final code review and merge decisions remain with engineers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.