No-code AI agent delivery workspace

For

Operations and IT teams that need working AI agents but have no dedicated engineering capacity

Solves

Automation needs sit in a queue while teams rent several agent tools that each cover only part of the build, test, deploy and monitor cycle.

Delivers

Deployed, monitored agent with editable code

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 the time from described task to a monitored, budgeted agent in production.

1. Build agents from plain-language task descriptions.
2. Start from pre-made templates and customize them.
3. Preview each step and review past executions.
4. Carry context between runs with state management.
5. Connect external services and tools.
6. Perform real actions such as sending email or updating records.
7. Embed agents in apps, sites or dashboards.
8. Set spending caps per agent or task.
9. Monitor runs live with detailed action logs.
10. Expose generated code for inspection and editing.
11. Test in a built-in sandbox with dry runs.
12. Orchestrate multiple agents across multi-step tasks.
13. Generate and revise text in the user's style and tone.
14. Produce articles, posts and captions as agent output.
15. Give real-time writing suggestions and edits.
16. Compare the reviewed result with the recorded baseline and value assumptions.
17. Capture corrections and named-owner approval before consequential use.
18. Export a versioned deployed, monitored agent with editable code with source references and unresolved questions.

Everything these tools do, in one app

• No-code agent builder Build AI agents without writing code using visual or conversational interfaces.Found in Broxi AI, Vellum, QuickAgent and 3 more
• Natural language agent creation Create agents by describing tasks in plain English or typing prompts.Found in Vellum, String.com, Promptius AI
• Pre-made templates Use ready-made templates that can be customized for specific needs.Found in Broxi AI, Vellum, Cotera
• Instant deployment Deploy agents immediately without manual coding or complex setup.Found in Broxi AI, String.com, QuickAgent and 2 more
• Multiple execution modes Run agents via UI, API triggers, or scheduled runs.Found in Vellum
• Step preview and history Inspect each step an agent will take and review past executions.Found in Vellum
• State management Carry context between runs to support more complex workflows.Found in Vellum
• Wide integration support Connect to many external services and tools.Found in Broxi AI, SmythOS, Vellum and 5 more
• Action-oriented agents Agents perform real actions like sending emails or managing data, not just chat.Found in Kodey.ai
• Embed agents in apps Launch agents inside applications, websites, or dashboards.Found in Kodey.ai
• Budget controls Set spending caps at the agent or task level.Found in Cotera
• Real-time monitoring Track agent behavior with live monitoring and detailed action logs.Found in Cotera
• Editable code output Expose generated code (e.g., Python) for technical users to inspect and modify.Found in Promptius AI, Brick Coder AI
• Built-in IDE and sandbox Test agents locally with scenario triggers and dry runs before deployment.Found in Promptius AI
• Multi-agent workflow support Orchestrate multiple agents with separated responsibilities for multi-step tasks.Found in Promptius AI
• Enterprise-grade security Ensure data protection with enterprise-level security measures.Found in Broxi AI
• Integrated virtual assistant Manage schedules, reminders, and quick data retrieval.Found in SmythOS
• Customizable dashboards Provide real-time insights and analytics through customizable dashboards.Found in SmythOS
• Smart file organization Organize files with AI-based tagging and search functionality.Found in SmythOS
• Context-aware text generation Generate text that adapts to the user's input style and intent.Found in Alice
• Multiple content types Support various content types like articles, blog posts, and social media captions.Found in Alice
• Real-time writing suggestions Provide real-time suggestions and edits to improve grammar and readability.Found in Alice
• Customizable tone settings Allow users to select formal, casual, or professional writing styles.Found in Alice

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-language task descriptions, sample data, service credentials and spending limits

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect plain-language task descriptions

4. 3

   Sample data

5. 4

   Service credentials and spending limits

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Deployed, monitored agent with editable code

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 integration set and one deployment target; final action authorization and production release remain with the buyer's named owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent brief and connections, Editable build and test preview, Client run dashboard and delivery. Use a thumbnail gallery for agents, a large central build canvas with step preview, and a right-hand panel for integrations, budgets and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant step. Make the task-specific outcome a deployed, monitored agent with editable code visible beside its evidence, review state and value baseline.

Accounts and administration

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

Buyer-owned service accounts, authorized sample data and permitted internal systems. Cloud storage, ticketing and messaging 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

   7 days

   One buyer segment, one recurring use case; first modules: build agents from plain-language task descriptions; start from pre-made templates and customize them. 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 IT teams that need working AI agents but have no dedicated engineering capacity use it to solve "automation needs sit in a queue while teams rent several agent tools that each cover only part of the build, test, deploy and monitor cycle"?
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: Working agents in production per delivery week and manual hours removed per accepted agent.
4. Measure, then decide. Track working agents in production per delivery week and manual hours removed per accepted agent; 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 action authorization and production release remain with the buyer's named owner. Implement one approved input format, a bounded representative case set and the first two task modules: build agents from plain-language task descriptions; start from pre-made templates and customize them. Support the third module with operator review: preview each step and review past executions. 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 agent with editable code. Retain the explicit scope boundary: One approved integration set and one deployment target; final action authorization and production release remain with the buyer's named owner.

What the build depends on. Agent upload and preview, asynchronous run jobs, editable version history, reviewer access and tested export formats. High-fidelity production 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 action authorization and production release remain with the buyer's named owner.

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 multi-agent or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded deployed, monitored agent with editable code. 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 time from described task to a monitored, budgeted agent in production. Demonstrate a concrete deployed, monitored agent with editable code using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations and IT teams without dedicated engineering capacity 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 agent with editable code from a small authorized input set, with a transparent calculation of working agents in production per delivery week and manual hours removed per accepted agent and no promised savings.

The first 30 days

1. Week 1: interview five operations and IT teams without dedicated engineering capacity and inspect a recent example of automation needs sitting in a queue while teams rent several agent tools that each cover only part of the build, test, deploy and monitor cycle.
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 working agents in production per delivery week and manual hours removed per accepted agent, 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: Working agents in production per delivery week and manual hours removed per accepted agent. 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

Working agents in production per delivery week and manual hours removed per accepted agent; 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 agent with editable code. 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 IT teams without dedicated engineering capacity. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Broxi AI, SmythOS, Vellum, String.com, QuickAgent, Brick Coder AI, Kodey.ai, Cotera, Promptius AI and Alice, plus freelance developers and internal engineering queues. Compare this product with the buyer's present method on working agents in production per delivery week and manual hours removed per accepted agent. 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 and sandbox 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 a deployed, monitored agent with editable code. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, action accuracy and usage permissions. The buyer's named owner approves substantive actions and production scope. One approved integration set and one deployment target; final action authorization and production release remain with the buyer's named owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.