Remote AI agent approval coordination portal

For

Engineering teams running AI coding agents on shared hosts

Solves

AI coding agents block on permission prompts and questions that only a human at the keyboard can answer, so work stalls when nobody is at the terminal.

Delivers

Routed, policy-checked approval decisions

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

Keep agents moving while a named human stays in control of risky actions.

1. Route agent permission prompts and questions to a phone or another channel.
2. Push lock-screen notifications when an agent needs attention or input.
3. Hold risky commands until a human responds.
4. Support multiple AI coding agents and CLI tools.
5. Apply per-tool approval policies with path-level granularity.
6. Auto-approve low-risk operations such as reads under a specified path.
7. Deny specified dangerous operations outright.
8. Match rules by specificity with explicit rules overriding defaults.
9. Collect requests from all agents and machines into one inbox.
10. Route approvals to Slack or Teams.
11. Record every question and answer with the matched policy target and export it.
12. Fall back to terminal, web dashboard or Slack on timeout.
13. Pair a phone with a terminal by QR code.
14. Provide native iPhone and Android apps.
15. Fail closed when a notification is missing or cleared.
16. Mirror the live terminal session.
17. Show git diffs on the phone.
18. Edit files from the phone.
19. Accept speech-to-text input.
20. Connect over an encrypted tunnel with no relay server.
21. Run multiple concurrent sessions with independent notifications.
22. Store common commands as shortcuts.
23. Persist sessions across network drops and restore terminal state.
24. Intercept tool calls as an MCP proxy.
25. Let teams choose automation only, approval gates only, or both.
26. Keep detailed logs and captured request payloads.
27. Route requests to individuals, teams or channels.
28. Score request risk with a model to reduce manual approval volume.

Everything these tools do, in one app

• Remote approval of agent actions Lets a human approve or deny what an AI agent wants to do from a phone or another channel instead of being at the keyboard.Found in Pushary, CodeMote, Preloop
• Lock screen notifications Pushes an alert to the phone's lock screen when the agent needs attention or input.Found in Pushary, CodeMote
• Enforced gating before execution Holds a risky command or action until the human responds, so it cannot proceed unanswered.Found in Pushary, Preloop
• Works with multiple agents Supports several different AI coding agents or CLI tools rather than being tied to one vendor.Found in Pushary, CodeMote
• Per-tool approval policies Lets you set rules for each tool, including path-level granularity, to control which actions need approval.Found in Pushary
• Auto-approve safe operations Automatically approves low-risk actions such as reads under a specified path.Found in Pushary
• Deny dangerous operations Blocks specified dangerous operations outright instead of sending them for approval.Found in Pushary
• Rule specificity and overrides Matches rules by specificity, with explicit rules overriding defaults.Found in Pushary
• Unified approval inbox Collects approval requests from all agents and machines into a single queue.Found in Pushary
• Approval via Slack or Teams Routes approval requests to Slack or Teams so they can be answered there.Found in Pushary, Preloop
• Exportable audit trail Records every question and answer, including the policy target that matched, and lets you export it.Found in Pushary
• Fallback channels on timeout If the phone is unreachable, approvals can time out and fall back to the terminal, web dashboard, or Slack.Found in Pushary
• QR-code pairing Pairs the phone with a terminal by scanning a QR code instead of copying API keys.Found in Pushary
• Native mobile apps Provides native iPhone and Android apps rather than relying on web push.Found in Pushary
• Fail-closed behavior Missing or clearing a notification denies or waits, never auto-approves.Found in Pushary
• Live terminal session mirror Streams the terminal output in real time so you can watch the agent work from the phone.Found in CodeMote
• Git diff viewer Shows git diffs on the phone so you can review changes.Found in CodeMote
• File editor Lets you edit files from the phone.Found in CodeMote
• Speech-to-text input Lets you dictate text instead of typing on the phone keyboard.Found in CodeMote
• Direct encrypted connection Connects over an encrypted tunnel with no relay server, so code never touches the developer's infrastructure.Found in CodeMote
• Multiple concurrent sessions Runs several terminals at once, each with its own Live Activity and independent push notifications.Found in CodeMote
• Command shortcuts Stores common commands as shortcuts for quick reuse.Found in CodeMote
• Session persistence across network drops Keeps the agent running on the host and restores the full terminal state when you reconnect.Found in CodeMote
• MCP proxy architecture Intercepts tool calls by acting as an MCP proxy, working with existing MCP clients and servers with minimal configuration changes.Found in Preloop
• Flexible operation modes Lets teams choose automation only, approval gates only, or both combined.Found in Preloop
• Audit logs and payload capture Keeps detailed logs and captured request payloads for compliance and forensic review.Found in Preloop
• Approval routing to people or teams Routes approval requests to individuals, teams, or channels to avoid single-person bottlenecks.Found in Preloop
• Model-assisted risk scoring Planned option to use a model to score risk and reduce manual approval volume.Found in Preloop

What goes in, what comes out

The workflow

1. In
   Start with

   Agent configurations, host inventory, policy rules and routing targets

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent configurations

4. 3

   Host inventory

5. 4

   Policy rules and routing targets

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Routed, policy-checked approval decisions

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 policy matching, gating, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Model-assisted risk scoring is a planned option; final approval and denial remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent and host registry, Policy and routing rules, Approval inbox, Audit and export. Use a queue list for pending requests, a detail pane showing the command, path, agent, host and matched policy, and a right-hand panel for decision, comment and fallback channel. Let users compare a request against its policy target and prior decisions. Display pending, approved, denied, timed-out and fallback states. Provide a phone lock-screen action and a Slack or Teams message with the same decision controls. Make the task-specific outcome routed, policy-checked approval decisions visible beside its evidence, review state and value baseline.

Accounts and administration

Organization ownership, agent and host registry, policy versions, routing targets, approval states, fallback settings, session 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

Agent-owned configurations, authorized host access and permitted notification channels. Cloud secret storage, Slack and Teams, git providers and MCP clients and servers. 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: route agent permission prompts and questions to a phone or another channel; push lock-screen notifications when an agent needs attention or input. 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 on shared hosts use it to solve "AI coding agents block on permission prompts and questions that only a human at the keyboard can answer, so work stalls when nobody is at the terminal"?
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: Approval latency, unanswered-prompt rate and reviewer correction time.
4. Measure, then decide. Track approval latency and unanswered-prompt rate and reviewer correction time; 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 agent type, one host group and one routing channel; final approval and denial remain human. Implement one approved input format, a bounded representative case set and the first two task modules: route agent permission prompts and questions to a phone or another channel; push lock-screen notifications when an agent needs attention or input. Support the third module with operator review: hold risky commands until a human responds. 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 agents and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around routed, policy-checked approval decisions. Retain the explicit scope boundary: One agent type, one host group and one routing channel; final approval and denial remain human.

What the build depends on. Agent hook installation, notification delivery, encrypted tunnel, session persistence, 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 agent type, one host group and one routing channel; final approval and denial remain human.

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 and host group. Offer a monthly production allowance after repeat demand. Quote complex multi-agent or compliance work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded routed, policy-checked approval decision workflow. 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

Keep agents moving while a named human stays in control of risky actions. Demonstrate a concrete routed, policy-checked approval decision 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 on shared hosts professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample routed, policy-checked approval decision from a small authorized input set, with a transparent calculation of approval latency, unanswered-prompt rate and reviewer correction time and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams running AI coding agents on shared hosts and inspect a recent example of AI coding agents block on permission prompts and questions that only a human at the keyboard can answer, so work stalls when nobody is at the terminal.
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 approval latency, unanswered-prompt rate and reviewer correction time, 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: Approval latency, unanswered-prompt rate and reviewer correction time. 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

Approval latency, unanswered-prompt rate and reviewer correction time; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs routed, policy-checked approval decisions. 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 policies, 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 on shared hosts. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Pushary, CodeMote and Preloop. Compare this product with the buyer's present method on approval latency, unanswered-prompt rate and reviewer correction time. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Notification delivery, tunnel and session hosting, 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 routed, policy-checked approval decisions. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve agent permissions, source attribution, command accuracy and usage permissions. Named humans approve substantive actions and execution scope. One agent type, one host group and one routing channel; final approval and denial remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.