Visual multi-agent workflow delivery workspace

For

Engineering teams and automation builders running AI workflows and multi-agent systems

Solves

Teams rent several separate tools to build, test, deploy and monitor AI workflows, and their data and version history sit outside their control.

Delivers

A deployed, monitored multi-agent workflow owned by the buyer

Built in

about 6 weeks of creation time, MVP in 7 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

Reduce tool sprawl and keep workflow data, versions and traces inside the buyer's own environment.

1. Build AI workflows by dragging and dropping components.
2. Manage multiple agents working together in one system.
3. Connect local and external language models and data sources.
4. Edit underlying Python code for advanced control.
5. Run workflows locally on the desktop for privacy and offline use.
6. Test and debug workflows in real time as they run.
7. Export and share workflows for collaboration or deployment.
8. Deploy workflows to cloud and serverless edge targets.
9. Self-host the platform on the buyer's own infrastructure.
10. Apply role-based access control to users and projects.
11. Track workflow and agent versions with integrated versioning.
12. Run data ETL and connect vector databases.
13. Manage session memory across agent conversations.
14. Apply fallbacks, retries, A/B tests and parallel model execution.
15. Sync versions and environments through GitHub.
16. Expose an open-source SDK and federated GraphQL API.
17. Record real-time traces, experiment logs and prompt edits.
18. Launch from one-click AgentKit templates.
19. Break complex problems into recursive hierarchical steps.
20. Start from pre-built task, research and finance agent examples.
21. Run automated data analysis with customizable parameters.
22. Connect third-party applications into workflows.
23. Interpret natural language commands for workflow actions.
24. Monitor performance and return feedback in real time.
25. Access the workspace from multiple devices via the cloud.

Everything these tools do, in one app

• Visual workflow builder Lets users create AI workflows by dragging and dropping components instead of writing code.Found in Langflow Desktop, Langflow, Lamatic 3.0
• Multi-agent system support Enables building and managing systems where multiple AI agents work together.Found in Langflow, Lamatic 3.0, ROMA
• Multiple model integrations Connects to various language models and data sources, including local and external APIs.Found in Langflow Desktop, Langflow, ROMA
• Python customization Provides full access to Python code for advanced modifications and control.Found in Langflow
• Local execution Runs on the user's desktop for data privacy and offline access.Found in Langflow Desktop
• Real-time testing and debugging Allows testing and debugging workflows within the environment as they run.Found in Langflow Desktop
• Workflow export and sharing Enables exporting and sharing workflows for collaboration or deployment.Found in Langflow Desktop
• Cloud deployment Offers deployment to the cloud, including serverless edge options.Found in Langflow, Lamatic 3.0
• Self-hosting Allows users to host the platform on their own infrastructure.Found in Langflow
• Role-based access control Manages user permissions and access within the platform.Found in Lamatic 3.0
• Integrated versioning Tracks changes and versions of workflows or agents.Found in Lamatic 3.0
• Data ETL and vector DB support Includes built-in data extraction, transformation, loading, and vector database support.Found in Lamatic 3.0
• Session memory management Manages memory across sessions for agents.Found in Lamatic 3.0
• Agent optimization toolkit Provides fallbacks, retries, A/B testing, and parallel model execution for reliable runs.Found in Lamatic 3.0
• GitHub-native version control Integrates with GitHub for version control and environment support.Found in Lamatic 3.0
• Open-source SDK and GraphQL API Offers an SDK and a federated GraphQL API for integration.Found in Lamatic 3.0
• Real-time traces and logging Provides real-time traces, experiment logging, and a Prompt IDE.Found in Lamatic 3.0
• AgentKit templates Includes 1-click templates for quick agent launch.Found in Lamatic 3.0
• Recursive hierarchical structure Breaks down complex problems using a recursive, hierarchical approach.Found in ROMA
• Pre-built agent examples Provides ready-made examples like task solvers, research agents, and finance agents.Found in ROMA
• Automated data analysis Performs data analysis automatically with customizable parameters.Found in Clevrr Computer
• Third-party app integration Connects with popular third-party applications for seamless workflows.Found in Clevrr Computer
• Natural language command processing Interprets user commands using natural language processing.Found in Clevrr Computer
• Real-time performance monitoring Monitors performance and provides feedback in real time.Found in Clevrr Computer
• Cloud-based access Enables access from multiple devices via the cloud.Found in Clevrr Computer

What goes in, what comes out

The workflow

1. In
   Start with

   Workflow definitions, model connections, code modules and operational settings

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect workflow definitions

4. 3

   Model connections

5. 4

   Code modules and operational settings

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   A deployed, monitored multi-agent workflow owned by the buyer

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 fixed deployment target and approved model list; final architecture and production checks remain engineering. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Workflow canvas, Run and trace inspector, Deployment and access console. Use a thumbnail gallery for projects, a large central drag-and-drop canvas, and a right-hand panel for components, model settings and code. Let users compare workflow versions side by side. Display draft, tested, deployed and failed states. Provide a shared run link with traces anchored to the relevant step. Make the task-specific outcome a deployed, monitored multi-agent workflow owned by the buyer visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, component versions, run history, access roles, deployment targets, usage allowances, export logs 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

Buyer-owned repositories, model providers and permitted data sources. Cloud and edge deployment targets, GitHub, vector databases and third-party applications. 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

   7 days

   One buyer segment, one recurring use case; first modules: build AI workflows by dragging and dropping components; manage multiple agents working together in one system. 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 engineering teams and automation builders running AI workflows and multi-agent systems use it to solve "teams rent several separate tools to build, test, deploy and monitor AI workflows, and their data and version history sit outside their 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: Deployed workflows per builder hour and failed runs after release.
4. Measure, then decide. Track deployed workflows per builder hour and failed runs after release; 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 fixed deployment target and approved model list; final architecture and production checks remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: build AI workflows by dragging and dropping components; manage multiple agents working together in one system. Support the third module with operator review: connect local and external language models and data sources. 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 a deployed, monitored multi-agent workflow owned by the buyer. Retain the explicit scope boundary: One fixed deployment target and approved model list; final architecture and production checks remain engineering.

What the build depends on. Workflow upload and preview, asynchronous run jobs, editable version history, 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 fixed deployment target and approved model list; final architecture and production checks remain engineering.

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-agent or self-hosted deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded deployed, monitored multi-agent workflow owned by the buyer. 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 tool sprawl and keep workflow data, versions and traces inside the buyer's own environment. Demonstrate a concrete deployed, monitored multi-agent workflow owned by the buyer using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams and automation builders running AI workflows and multi-agent systems professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample deployed, monitored multi-agent workflow owned by the buyer from a small authorized input set, with a transparent calculation of deployed workflows per builder hour and failed runs after release and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams and automation builders running AI workflows and multi-agent systems and inspect a recent example of teams renting several separate tools to build, test, deploy and monitor AI workflows, and their data and version history sit outside their control.
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 deployed workflows per builder hour and failed runs after release, 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: Deployed workflows per builder hour and failed runs after release. 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

Deployed workflows per builder hour and failed runs after release; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a deployed, monitored multi-agent workflow owned by the buyer. 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 workflow patterns, deployment constraints 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 and automation builders running AI workflows and multi-agent systems. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Langflow Desktop, Langflow, Lamatic 3.0, ROMA and Clevrr Computer, which buyers rent separately today. Compare this product with the buyer's present method on deployed workflows per builder hour and failed runs after release. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, compute and 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 a deployed, monitored multi-agent workflow owned by the buyer. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, code accuracy and usage permissions. Engineering owners approve substantive changes and deployment scope. One fixed deployment target and approved model list; final architecture and production checks remain engineering. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.