Agentic workflow automation control workspace

For

Operations leads and small delivery teams automating repetitive computer tasks and multi-step workflows

Solves

Repetitive computer tasks and multi-step workflows are split across several rented automation tools, so work, permissions and results stay outside the team's control.

Delivers

Reviewed, scheduled agent runs with reusable programs and rollback

Built in

about 6 weeks of creation time, MVP in 7 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 repetition while keeping every automated step visible and reversible.

1. Accept plain-language requests.
2. Build workflows by drag and drop.
3. Define custom triggers and actions.
4. Connect approved apps and services.
5. Automate repetitive manual tasks.
6. Monitor runs live and handle errors.
7. Report workflow performance.
8. Show reasoning for each request.
9. Run on web, desktop and mobile.
10. Export web apps, charts, PDFs, Excel and HTML.
11. Recognize and optimize automation sequences.
12. Reduce routine human error.
13. Cover research, monitoring, reporting and social tracking.
14. Encode completed tasks into reusable programs.
15. Roll back to previous workflow versions.
16. Set granular file and service access.
17. Execute multi-step tasks autonomously.
18. Schedule runs and send reminders.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned reviewed agent run record with source references and unresolved questions.

Everything these tools do, in one app

• Natural language input Allows users to give commands or requests in plain language.Found in ComputerX, Ema
• Drag-and-drop workflow builder Lets users create automation workflows visually without coding.Found in Wispr Flow for Windows, Autoflow, Wispr Flow
• Customizable triggers and actions Enables users to define specific events that start workflows and the actions they perform.Found in Wispr Flow for Windows, Autoflow, Wispr Flow
• Integration with apps and services Connects to various applications and platforms to automate tasks across them.Found in Wispr Flow for Windows, Autoflow, TailorTask and 2 more
• Task automation Automates repetitive manual tasks to save time and reduce effort.Found in Autoflow, TailorTask, Adaptive — The Agent Computer and 1 more
• Real-time monitoring and error handling Provides live feedback on workflow status and manages errors during execution.Found in Wispr Flow for Windows, Wispr Flow
• Analytics and reporting Offers detailed insights and reports on workflow performance.Found in Wispr Flow
• Transparent task execution Shows how the AI reasons and completes each request for visibility.Found in ComputerX
• Cross-platform availability Accessible on multiple devices such as web, desktop, and mobile.Found in ComputerX
• Versatile output formats Generates outputs like web apps, charts, PDFs, Excel files, and HTML exports.Found in ComputerX
• AI-driven task recognition Uses AI to recognize and optimize automation sequences.Found in Wispr Flow for Windows
• Error reduction Minimizes human error by automating routine operations.Found in Autoflow
• Automation across domains Handles tasks in areas like content research, company monitoring, report generation, and social media tracking.Found in TailorTask
• Encoded Memory Converts learnings from completed tasks into reusable programs for faster repeats.Found in Adaptive — The Agent Computer
• Production rollback controls Allows reverting to previous workflow versions when needed.Found in Adaptive — The Agent Computer
• Granular access controls Lets users choose which files or services the agent may access.Found in Adaptive — The Agent Computer
• Autonomous task execution Performs multi-step tasks without manual intervention.Found in Adaptive — The Agent Computer, ChatGPT agent
• Automated scheduling and reminders Manages calendars and sends smart reminders based on preferences.Found in Ema

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-language requests, connected app credentials, approved access rules and scheduling preferences

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect plain-language requests

4. 3

   Connected app credentials

5. 4

   Approved access rules and scheduling preferences

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, scheduled agent runs with reusable programs and rollback

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. One approved app set and one scheduling policy; final approvals and external actions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Request and access setup, Workflow builder and run monitor, Outputs and audit log. Use a project list, a central drag-and-drop canvas, and a right-hand panel for triggers, connected apps, permissions and comments. Let users compare workflow versions side by side. Display draft, running, needs review and rolled back states. Provide a client preview link with comments anchored to the relevant run. Make the task-specific outcome reviewed, scheduled agent runs with reusable programs and rollback visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, workflow versions, connected app scopes, run history, approval states, usage allowances, rollback records 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 app accounts, authorized file sources and permitted scheduling services. Cloud storage, spreadsheet and document import/export and messaging 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

   7 days

   One buyer segment, one recurring use case; first modules: accept plain-language requests; build workflows by drag and drop. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

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

4. 4

   Full product

   3 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 operations leads and small delivery teams automating repetitive computer tasks and multi-step workflows use it to solve "repetitive computer tasks and multi-step workflows are split across several rented automation tools, so work, permissions and results stay outside the team's control"?
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: Completed runs per operator hour and manual corrections per completed run.
4. Measure, then decide. Track completed runs per operator hour and manual corrections per completed run; 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 approved app set and one scheduling policy; final approvals and external actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: accept plain-language requests; build workflows by drag and drop. Support the remaining modules with operator review: define custom triggers and actions; connect approved apps and services; automate repetitive manual tasks; monitor runs live and handle errors. 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, scheduled agent runs with reusable programs and rollback. Retain the explicit scope boundary: One approved app set and one scheduling policy; final approvals and external actions remain human.

What the build depends on. Asset upload and preview, asynchronous agent jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist operations QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved app set and one scheduling policy; final approvals and external actions 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 workflow package. Offer a monthly production allowance after repeat demand. Quote complex multi-app or specialist automation separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, scheduled agent run with reusable programs and rollback. 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 repetition while keeping every automated step visible and reversible. Demonstrate a concrete reviewed, scheduled agent run with reusable programs and rollback using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations leads and small delivery teams professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, scheduled agent run with reusable programs and rollback from a small authorized input set, with a transparent calculation of completed runs per operator hour and manual corrections per completed run and no promised savings.

The first 30 days

1. Week 1: interview five operations leads and small delivery teams automating repetitive computer tasks and multi-step workflows and inspect a recent example of repetitive computer tasks and multi-step workflows are split across several rented automation tools, so work, permissions and results stay outside the team's control.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure completed runs per operator hour and manual corrections per completed run, 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: Completed runs per operator hour and manual corrections per completed run. 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

Completed runs per operator hour and manual corrections per completed run; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, scheduled agent runs with reusable programs and rollback. 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 workflows, connected app mappings and review examples, together with reliable delivery for a narrow operations niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for operations leads and small delivery teams automating repetitive computer tasks and multi-step workflows. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

ComputerX, Wispr Flow for Windows, Autoflow, TailorTask, Adaptive — The Agent Computer, Wispr Flow, ChatGPT agent and Ema. Compare this product with the buyer's present method on completed runs per operator hour and manual corrections per completed run. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Agent run attempts, connected app usage, 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, scheduled agent runs with reusable programs and rollback. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, access permissions and audit accuracy. Named owners approve substantive changes and external actions. One approved app set and one scheduling policy; final approvals and external actions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.