Scheduled multi-machine agent operations portal

For

Engineering teams running recurring software tasks with AI coding agents

Solves

Recurring software tasks run by AI agents need scheduling, isolation, cost limits and human approval across several machines, and today that coordination is manual or split across separate tools.

Delivers

Reviewed agent runs with logs, cost reports and approval records

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce manual coordination of recurring agent work while keeping cost, credentials and risky steps under control.

1. Schedule agent runs from minutes to weekly.
2. Run agents unattended on a recurring cadence.
3. Isolate each run in a sandbox.
4. Store and reuse memory between runs.
5. Stream logs live during execution.
6. Connect external tools through MCP, CLI, SDK or API.
7. Configure the environment with a setup script.
8. Book jobs on a calendar with fixed slots.
9. Skip a slot when the previous run still awaits approval.
10. Block sensitive credentials from the agent process.
11. Enforce dollar, turn and wall-clock budget caps.
12. Pause on risky steps for human approval or rejection.
13. Report actual dollar cost after each run.
14. Distribute runs across owned laptops, build servers and VMs.
15. Route tasks to premium, cheap or local models by tier.
16. Resume workflows after stalls or crashes.
17. Plan work from an issue tracker and fix bugs in its own codebase.
18. File bugs back to the issue tracker for the next cycle.

Everything these tools do, in one app

• Scheduled agent execution Runs AI agents automatically on a recurring schedule, from every few minutes to weekly.Found in Cronloop AI, Clockwork
• Unattended operation Agents work autonomously without requiring a person to be present.Found in Cronloop AI, Clockwork, apra-fleet
• Sandboxed execution Each agent run happens in an isolated environment to contain its actions.Found in Cronloop AI, Clockwork
• Memory between runs Agents can store and reuse information from previous runs to improve future performance.Found in Cronloop AI
• Real-time log streaming Lets you monitor agent activity live as it happens.Found in Cronloop AI
• Connector support Integrates with many external tools through standard protocols like MCP, CLI, SDK, or API.Found in Cronloop AI
• Setup script configuration Allows customizing the agent environment with a shell script for dependencies or settings.Found in Cronloop AI
• Calendar-based scheduling Books agent jobs on a calendar with specific time slots.Found in Clockwork
• Skip slot if pending Skips a scheduled run if the previous one is still waiting for approval.Found in Clockwork
• Credential isolation Blocks sensitive credentials from reaching the agent process during execution.Found in Clockwork
• Budget caps Enforces hard limits on dollar cost, number of turns, and wall-clock time for each run.Found in Clockwork
• Pause on risk Pauses the agent when a risky step is detected and waits for human approval or rejection.Found in Clockwork
• Post-run cost report Provides a report after each run showing the actual dollar cost of the work.Found in Clockwork
• Multi-machine fleet Runs agents across multiple computers you already own, such as laptops, build servers, and VMs.Found in apra-fleet
• Mixed provider tiers Uses different AI models for different tasks, like premium models for planning and cheap or local models for mechanical work.Found in apra-fleet
• Durable resumable workflows Workflows can survive stalls and crashes, resuming from where they left off.Found in apra-fleet
• Self-building capability The tool can plan work from an issue tracker and fix bugs in its own codebase.Found in apra-fleet
• Issue tracker integration Agents plan work from an issue tracker and file bugs for the next cycle.Found in apra-fleet

What goes in, what comes out

The workflow

1. In
   Start with

   Issue tracker, repository access, machine inventory and run policies

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect an issue tracker

4. 3

   Repository access

5. 4

   Machine inventory and run policies

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed agent runs with logs, cost reports and approval records

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 repository and one issue tracker per pilot; final code review and merge decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Fleet and schedule board, Run detail and live log, Approval and cost review. Use a calendar and machine list for jobs, a central run view with streaming logs, and a right-hand panel for budgets, credentials policy and approval state. Let users compare runs side by side. Display scheduled, running, paused, approved and failed states. Provide a client preview link with comments anchored to the relevant run. Make the task-specific outcome reviewed agent runs with logs, cost reports and approval records visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, machine inventory, schedule versions, approval states, credential policies, budget caps, run 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

Customer-owned repositories, issue trackers and machine inventory. Cloud asset storage, design-file import/export and publishing destinations. 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: schedule agent runs from minutes to weekly; run agents unattended on a recurring cadence. 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 recurring software tasks with AI coding agents use it to solve "recurring software tasks run by AI agents need scheduling, isolation, cost limits and human approval across several machines, and today that coordination is manual or split across separate tools"?
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 runs per operator hour and rework after unattended execution.
4. Measure, then decide. Track accepted agent runs per operator hour and rework after unattended execution; 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 and one issue tracker per pilot; final code review and merge decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: schedule agent runs from minutes to weekly; run agents unattended on a recurring cadence. Support the third module with operator review: isolate each run in a sandbox. 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 agent runs with logs, cost reports and approval records. Retain the explicit scope boundary: One repository and one issue tracker per pilot; final code review and merge decisions remain human.

What the build depends on. Asset upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist creative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One repository and one issue tracker per pilot; final code review and merge decisions 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 repository and issue tracker. Offer a monthly run allowance after repeat demand. Quote complex multi-machine or self-modifying workflows separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed agent runs with logs, cost reports and approval records. 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 manual coordination of recurring agent work while keeping cost, credentials and risky steps under control. Demonstrate a concrete reviewed agent runs with logs, cost reports and approval records using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams running recurring software tasks with AI coding agents professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed agent runs with logs, cost reports and approval records from a small authorized input set, with a transparent calculation of accepted agent runs per operator hour and rework after unattended execution and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams running recurring software tasks with AI coding agents and inspect a recent example of recurring software tasks run by AI agents need scheduling, isolation, cost limits and human approval across several machines, and today that coordination is manual or split across separate tools.
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 runs per operator hour and rework after unattended execution, 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 runs per operator hour and rework after unattended execution. 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 runs per operator hour and rework after unattended execution; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed agent runs with logs, cost reports and approval records. 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 run policies, machine profiles 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 recurring software tasks with AI coding agents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Cronloop AI, Clockwork and apra-fleet, plus cron jobs, CI runners and manual scripts. Compare this product with the buyer's present method on accepted agent runs per operator hour and rework after unattended execution. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, machine hours, 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 agent runs with logs, cost reports and approval records. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve code ownership, source attribution, license accuracy and usage permissions. Engineers approve substantive changes and deployment scope. One repository and one issue tracker per pilot; final code review and merge decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.