No-code agent build and deployment workspace

For

Operations and technical teams building custom AI agents without coding

Solves

Agent building is split across several rented tools, so instructions, workflows, documents, tests and deployments live in different places and cannot be owned or audited end to end.

Delivers

Deployed, monitored agents owned by the client

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 the number of rented tools and handoffs needed to build, test and deploy custom AI agents.

1. Create agents from plain-language instructions without code.
2. Configure tasks by describing them in natural language.
3. Design agent logic on a visual node-and-connection canvas.
4. Start from pre-built templates for common automation scenarios.
5. Attach built-in tools such as browser, calculator and database.
6. Train agents on uploaded documents and data.
7. Handle text, image and voice inputs.
8. Accept voice commands for agent interaction.
9. Connect agents to external apps and services.
10. Test sub-agents independently and evaluate performance.
11. Track agent behavior step-by-step for debugging.
12. Refine agents through manual or conversational edits.
13. Schedule proactive tasks, monitoring and follow-ups.
14. Publish agents to the web in one action.
15. Let multiple users work in a shared project workspace.
16. Browse and share third-party agents and tools in a marketplace.
17. Store agents and data locally for privacy and control.
18. Compare the reviewed result with the recorded baseline and value assumptions.
19. Capture corrections and named-owner approval before consequential use.
20. Export a versioned deployed, monitored agents owned by the client with source references and unresolved questions.

Everything these tools do, in one app

• No-code agent creation Build AI agents without writing code, using natural language or visual interfaces.Found in Nelly, Deforge, NexusGPT and 6 more
• Natural language instructions Create and configure agents by describing tasks in plain language.Found in Nelly, Okibi, Latitude 2.0 and 2 more
• Visual workflow builder Design agent logic through a visual interface with nodes and connections.Found in Deforge, Okibi, Portals
• Pre-built templates Start from ready-made templates for common automation scenarios.Found in Okibi, Portals
• One-click publishing Deploy agents instantly to the web with a single action.Found in Okibi
• Rapid deployment Create and launch agents in minutes rather than days.Found in NexusGPT, Dabe Agents, Latitude 2.0
• App integrations Connect agents to external applications and services to automate workflows.Found in Deforge, NexusGPT, Portals and 3 more
• Built-in tools Use included utilities like a browser, calculator, or database within agents.Found in Nelly, Portals
• Document training Train agents on your own documents and data for tailored responses.Found in ScalerX.ai, NexusGPT
• Multi-modal support Handle various media types such as text, images, and voice.Found in ScalerX.ai, NexusGPT
• Voice interaction Interact with agents using voice commands.Found in ZooClaw, ScalerX.ai
• Agent refinement Edit and improve agents after creation through manual or conversational adjustments.Found in Latitude 2.0
• Testing and evaluation Test sub-agents independently and evaluate their performance.Found in Latitude 2.0
• Observability Track agent behavior step-by-step for debugging and monitoring.Found in Latitude 2.0
• Proactive automation Schedule tasks, monitoring, and follow-ups that run without user input.Found in ZooClaw
• Collaboration tools Allow multiple users to work on agent projects within a shared workspace.Found in Deforge
• Marketplace Browse and share third-party agents and tools.Found in Nelly, NexusGPT
• Local data storage Store agents and data locally for privacy and control.Found in Nelly

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-language instructions, uploaded documents, integration credentials and workflow rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect plain-language instructions

4. 3

   Uploaded documents

5. 4

   Integration credentials and workflow rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Deployed, monitored agents owned by the client

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 integration set and one deployment target; final workflow approval and consequential actions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent brief and inputs, Visual workflow canvas, Test and evaluation, Deployment and monitoring. Use a thumbnail gallery for agent projects, a large central canvas for nodes and connections, and a right-hand panel for instructions, tools, documents and comments. Let users compare agent versions side by side. Display draft, in test, deployed and paused states. Provide a client preview link with comments anchored to the relevant agent step. Make the task-specific outcome deployed, monitored agents owned by the client visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, client comments, approval states, usage allowances, revision limits, deployment 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

Client-owned documents, authorized app credentials and permitted workflow data. Cloud storage, app connectors and deployment 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: create agents from plain-language instructions without code; design agent logic on a visual node-and-connection canvas. 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 and technical teams building custom AI agents without coding use it to solve "agent building is split across several rented tools, so instructions, workflows, documents, tests and deployments live in different places and cannot be owned or audited end to end"?
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 agents per build hour and post-deployment correction rate.
4. Measure, then decide. Track deployed agents per build hour and post-deployment correction rate; 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 integration set and one deployment target; final workflow approval and consequential actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: create agents from plain-language instructions without code; design agent logic on a visual node-and-connection canvas. Support the remaining modules with operator review: attach built-in tools and connect external apps; train agents on uploaded documents and data; test sub-agents independently and evaluate performance; track agent behavior step-by-step for debugging; refine agents through manual or conversational edits; schedule proactive tasks and publish agents to the web. 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 deployed, monitored agents owned by the client. Retain the explicit scope boundary: One approved integration set and one deployment target; final workflow approval and consequential actions remain human.

What the build depends on. Agent upload and preview, asynchronous build jobs, editable version history, reviewer access and tested export formats. High-fidelity deployment requires specialist integration QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved integration set and one deployment target; final workflow approval and consequential 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 agent package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist deployment separately. These are test prices, not market benchmarks. Package the initial sale as one bounded deployed, monitored agents owned by the client. 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 the number of rented tools and handoffs needed to build, test and deploy custom AI agents. Demonstrate a concrete deployed, monitored agents owned by the client using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations and technical teams building custom AI agents without coding 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 agents owned by the client from a small authorized input set, with a transparent calculation of deployed agents per build hour and post-deployment correction rate and no promised savings.

The first 30 days

1. Week 1: interview five operations and technical teams building custom AI agents without coding and inspect a recent example of agent building split across several rented 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 deployed agents per build hour and post-deployment correction rate, 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 agents per build hour and post-deployment correction rate. 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 agents per build hour and post-deployment correction rate; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs deployed, monitored agents owned by the client. 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 agent templates, integration 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 and technical teams building custom AI agents without coding. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Nelly, Deforge, NexusGPT, ScalerX.ai, Okibi, Portals, Latitude 2.0, Dabe Agents, ZooClaw and Chat Thing are what buyers use today, each covering part of the job. Compare this product with the buyer's present method on deployed agents per build hour and post-deployment correction rate. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, integration processing, 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 deployed, monitored agents owned by the client. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve client data ownership, source attribution, credential handling and usage permissions. Clients approve substantive workflow changes and deployment scope. One approved integration set and one deployment target; final workflow approval and consequential actions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.