Recorded task to reusable agent workbench

For

IT and development teams turning recorded computer tasks into reusable AI agents

Solves

Teams repeat the same computer tasks manually because turning a recorded demonstration into a reliable agent requires prompt engineering, dataset annotation and custom code.

Delivers

Editable, exportable agent workflow

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

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

Reduce manual repetition while keeping the workflow under team control.

1. Capture screen actions such as clicks, keystrokes and navigation.
2. Convert a recorded demonstration into a reusable agent workflow without code.
3. Build automation without manual prompt engineering or dataset annotation.
4. Identify interface elements by meaning and context rather than fixed coordinates.
5. Support tasks that span multiple applications and windows.
6. Accept long, multi-stage recordings and multiple recordings for one process.
7. Tolerate interruptions and small UI changes by inferring intent at runtime.
8. Analyze sampled frames, transitions and metadata to extract actions.
9. Use visual analysis to identify actions across apps and windows.
10. Let reviewers edit the step-by-step workflow before deployment.
11. Export the workflow in an agent-ready format such as SKILL.md.
12. Record and play back desktop workflows using natural-language commands.
13. Compile the workflow into a deterministic DSL for local runs with zero recurring run cost.
14. Provide an open-source CLI for DOM and browser automation.
15. Run routine executions offline with a local vision execution engine.
16. Offer a free recording entry point with signup credits.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned editable, exportable agent workflow with source references and unresolved questions.

Everything these tools do, in one app

• Screen recording capture Records your screen actions such as clicks, keystrokes, and navigation as you perform a task.Found in Trainer, SkillForge, Freu AI
• Demonstration to agent Converts a recorded demonstration into a reusable AI agent or workflow without writing code.Found in Trainer, SkillForge, Freu AI
• No prompts or labels Creates automation from demonstrations without manual prompt engineering or dataset annotation.Found in Trainer
• Semantic element recognition Identifies interface elements by their meaning and context rather than fixed pixel coordinates.Found in Trainer, Freu AI
• Multi-app workflow support Handles tasks that span multiple applications and windows.Found in SkillForge, Freu AI
• Multi-stage recordings Supports long or multi-stage recordings and allows multiple recordings to cover different parts of a process.Found in Trainer
• Interruption and variation handling Tolerates interruptions and small UI changes by inferring intent at runtime.Found in Trainer
• Frame and event analysis Analyzes sampled frames, transitions, and metadata to extract actions.Found in SkillForge
• Visual UI analysis Uses visual analysis to identify actions across multiple apps and windows.Found in SkillForge
• Workflow editing Lets you review and edit the step-by-step workflow before deployment.Found in SkillForge
• Agent-ready export Exports the workflow in a format usable by agent frameworks, such as SKILL.md.Found in SkillForge
• Natural-language commands Allows recording and playback of desktop workflows using natural-language commands.Found in Freu AI
• Ahead-of-time compilation Compiles the workflow into a deterministic DSL so subsequent runs execute locally with zero recurring run cost.Found in Freu AI
• Open-source CLI Provides an open-source command-line tool for DOM and browser automation.Found in Freu AI
• Local execution engine Plans to run routine executions offline using a local vision execution engine.Found in Freu AI
• Free entry point Offers a free plan or free recording plus signup credits to start.Found in Trainer, SkillForge

What goes in, what comes out

The workflow

1. In
   Start with

   Screen recordings, multi-app sessions and natural-language commands

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect screen recordings

4. 3

   Multi-app sessions and natural-language commands

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Editable, exportable agent workflow

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 approved recording format and one target agent framework; final workflow approval and deployment decisions remain with the team. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Recording capture, Workflow review and editing, Agent export and run. Use a thumbnail gallery for recordings, a large central step canvas, and a right-hand panel for element mappings, variations and comments. Let users compare recorded and inferred steps side by side. Display draft, reviewed and deployed states. Provide a run log with step-level evidence. Make the task-specific outcome an editable, exportable agent workflow visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, recording versions, reviewer comments, approval states, usage allowances, run limits, export 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 recordings, authorized application access and permitted research sources. Cloud storage, design-file import/export and agent framework destinations. 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

   6 days

   One buyer segment, one recurring use case; first modules: capture screen actions such as clicks, keystrokes and navigation; convert a recorded demonstration into a reusable agent workflow without code. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 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 IT and development teams turning recorded computer tasks into reusable AI agents use it to solve "teams repeat the same computer tasks manually because turning a recorded demonstration into a reliable agent requires prompt engineering, dataset annotation and custom code"?
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 recorded task and manual interventions per run.
4. Measure, then decide. Track accepted agent runs per recorded task and manual interventions per 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 recording format and one target agent framework; final workflow approval and deployment decisions remain with the team. Implement one approved input format, a bounded representative case set and the first two task modules: capture screen actions such as clicks, keystrokes and navigation; convert a recorded demonstration into a reusable agent workflow without code. Support the third module with operator review: build automation without manual prompt engineering or dataset annotation. 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 an editable, exportable agent workflow. Retain the explicit scope boundary: One approved recording format and one target agent framework; final workflow approval and deployment decisions remain with the team.

What the build depends on. Recording upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity automation requires specialist technical QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved recording format and one target agent framework; final workflow approval and deployment decisions remain with the team.

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 recording 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 editable, exportable agent 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

Reduce manual repetition while keeping the workflow under team control. Demonstrate a concrete editable, exportable agent workflow using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT and development teams turning recorded computer tasks into reusable AI agents professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample editable, exportable agent workflow from a small authorized input set, with a transparent calculation of accepted agent runs per recorded task and manual interventions per run and no promised savings.

The first 30 days

1. Week 1: interview five IT and development teams turning recorded computer tasks into reusable AI agents and inspect a recent example of teams repeat the same computer tasks manually because turning a recorded demonstration into a reliable agent requires prompt engineering, dataset annotation and custom code.
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 recorded task and manual interventions per 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: Accepted agent runs per recorded task and manual interventions per 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

Accepted agent runs per recorded task and manual interventions per run; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs an editable, exportable agent workflow. 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 element mappings, variation cases and review examples, together with reliable delivery for a narrow technical niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for IT and development teams turning recorded computer tasks into reusable AI agents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Trainer, SkillForge and Freu AI, plus manual scripting and generic automation tools. Compare this product with the buyer's present method on accepted agent runs per recorded task and manual interventions per run. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Recording processing, frame and event analysis, 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 an editable, exportable agent workflow. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve team intent, source attribution, action accuracy and usage permissions. Teams approve substantive changes and deployment scope. One approved recording format and one target agent framework; final workflow approval and deployment decisions remain with the team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.