Oversight-first local Mac agent console

For

Developers and technical teams running AI coding agents on their own Macs

Solves

Agents that control apps, browsers and files run without a clear approval path, so consequential actions are hard to review, interrupt or audit.

Delivers

Approved agent actions linked to source evidence

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 an AI coding agent on a Mac that can control apps, browsers and files with user oversight.

1. Run the agent locally on the user's own Mac.
2. Provide a graphical app window instead of only a terminal.
3. Connect different model providers or local models.
4. Accept extra tools through the Model Context Protocol.
5. Read, modify and organize local files.
6. Run shell commands and Python scripts.
7. Drive macOS applications, including Xcode.
8. Drive a browser to interact with websites.
9. Use existing logged-in browser sessions.
10. Let the user interrupt and take over browser actions mid-click.
11. Automate and test apps in the iOS Simulator, including tapping and swiping.
12. Approve agent requests from a phone.
13. Reach the agent remotely through Telegram.
14. Require confirmation for consequential actions unless autonomous mode is enabled.
15. Run each chat on its own git worktree and branch.
16. Keep chats and open files across restarts in a sidebar.
17. Delegate tasks to sub-agents.
18. Extend capabilities with reusable Skills.
19. Write Python in a persistent namespace for browser automation.
20. Batch browser operations per call.
21. Keep source code available under an open license for auditing and contribution.

Everything these tools do, in one app

• Local Mac agent Runs the AI agent directly on your own Mac machine.Found in Superagent, Naseem
• Graphical interface Provides a visual app window instead of only a terminal.Found in Superagent, Naseem
• Bring your own model Lets you connect the agent to different AI model providers or local models.Found in Naseem, OpenBrowser-AI
• MCP tool support Accepts extra tools through the Model Context Protocol.Found in Superagent, Naseem
• File system access Reads, modifies, and organizes files on your computer.Found in Naseem
• Terminal and Python execution Runs shell commands and Python scripts as part of tasks.Found in Naseem
• Native app control Drives macOS applications directly, including Xcode.Found in Naseem
• Browser control Drives a browser to interact with websites.Found in Superagent, OpenBrowser-AI
• Logged-in browser sessions Uses your existing browser sessions so the agent can access authenticated sites.Found in Superagent
• Take back control Lets you interrupt and take over the agent's browser actions mid-click.Found in Superagent
• iOS Simulator integration Automates and tests apps in the iOS Simulator, including tapping and swiping.Found in Superagent, Naseem
• Remote approval Lets you approve agent requests from your phone.Found in Superagent
• Telegram remote access Reach the agent remotely through Telegram.Found in Naseem
• Approval-first actions Requires user confirmation for consequential actions unless autonomous mode is enabled.Found in Naseem
• Git worktree isolation Runs each chat on its own git worktree and branch so your main checkout stays unchanged.Found in Superagent
• Session persistence Keeps chats and open files across restarts in a sidebar.Found in Superagent
• Sub-agent delegation Hands tasks to sub-agents.Found in Naseem
• Reusable Skills Uses reusable Skills to extend the agent's capabilities.Found in Naseem
• Persistent Python namespace LLM writes Python code in a persistent namespace for browser automation.Found in OpenBrowser-AI
• Batched browser operations Batches browser operations per call for efficiency.Found in OpenBrowser-AI
• Open source Source code is available under an open license for auditing and contribution.Found in Superagent, OpenBrowser-AI

What goes in, what comes out

The workflow

1. In
   Start with

   Approved model connections, local tools and session state

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved model connections

4. 3

   Local tools and session state

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Approved agent actions linked to source evidence

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final code changes, file writes and external actions remain under named human approval. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent session and task queue, Approval and audit console, Tool and model settings. Use a sidebar for chats, open files and worktrees, a large central transcript with source-linked tool calls, and a right-hand panel for approvals, model choice and permissions. Let users interrupt and take over browser actions mid-click. Display pending, approved, rejected and autonomous states. Provide a phone approval view and a Telegram remote view. Make the task-specific outcome approved agent actions linked to source evidence visible beside its review state and value baseline.

Accounts and administration

Project ownership, model connections, tool permissions, approval states, worktree and branch records, session history, usage allowances 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

User-owned Macs, model provider APIs, local models, MCP tool servers, browsers, macOS applications, iOS Simulator and git repositories. 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: run the agent locally on the user's own Mac; provide a graphical app window instead of only a terminal. 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 technical teams running AI coding agents on their own Macs use it to solve "agents that control apps, browsers and files run without a clear approval path, so consequential actions are hard to review, interrupt or audit"?
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: Approved actions per completed task and unapproved consequential actions.
4. Measure, then decide. Track approved actions per completed task and unapproved consequential 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 Mac, one model provider and one approved tool set; final code changes and external actions remain under named human approval. Implement one approved input format, a bounded representative case set and the first two task modules: run the agent locally on the user's own Mac; provide a graphical app window instead of only a terminal. Support the third module with operator review: connect different model providers or local models. 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 approved agent actions linked to source evidence. Retain the explicit scope boundary: One Mac, one model provider and one approved tool set; final code changes and external actions remain under named human approval.

What the build depends on. Local agent runtime, graphical interface, model connection layer, MCP tool registry, file and terminal access, browser and app control, iOS Simulator bridge, remote approval channel, git worktree management, session persistence, sub-agent delegation, Skills system, persistent Python namespace and batched browser operations. High-fidelity production requires specialist creative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One Mac, one model provider and one approved tool set; final code changes and external actions remain under named human approval.

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 agent setup. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist tooling separately. These are test prices, not market benchmarks. Package the initial sale as one bounded approved agent actions linked to source evidence. 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 an AI coding agent on a Mac that can control apps, browsers and files with user oversight. Demonstrate a concrete approved agent actions linked to source evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Developers and technical teams running AI coding agents on their own Macs professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample approved agent actions linked to source evidence from a small authorized input set, with a transparent calculation of approved actions per completed task and unapproved consequential actions and no promised savings.

The first 30 days

1. Week 1: interview five developers and technical teams running AI coding agents on their own Macs and inspect a recent example of agents that control apps, browsers and files run without a clear approval path, so consequential actions are hard to review, interrupt or audit.
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 approved actions per completed task and unapproved consequential 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: Approved actions per completed task and unapproved consequential 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

Approved actions per completed task and unapproved consequential actions; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs approved agent actions linked to source evidence. 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 tool permissions, review examples and verified operating constraints, together with reliable delivery for a narrow technical niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for developers and technical teams running AI coding agents on their own Macs. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Superagent, Naseem and OpenBrowser-AI. Compare this product with the buyer's present method on approved actions per completed task and unapproved consequential actions. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, local 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 approved agent actions linked to source evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve user intent, source attribution, code accuracy and usage permissions. Users approve substantive changes and external actions. One Mac, one model provider and one approved tool set; final code changes and external actions remain under named human approval. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.