AI app for it and development · no coding needed
Conversational agent build and operations workspace
Reduce the number of rented agent tools and keep agent definitions, conversations, traces and approvals in one owned workspace.
Made for: Product and platform teams building and running conversational AI agents for internal or customer use
What it does for you
The problem
Agent work is split across builder packages, orchestration libraries, evaluation scripts and separate hosting, so teams rent several tools and still cannot see, test or approve what agents do in production.
What it gives you
A deployed, monitored agent with human approval points
What you give it
Approved promptstool definitionsmodel settingsworkflow stepsreview rules
Build your own version of Open Agent Kit, Scoopika - TS packages and more
One app with what these 10 AI tools do, yours to keep and change: Open Agent Kit, Scoopika - TS packages, VoltAgent, Sim, Agent Development Kit, Zen Agents (by Zencoder), Retool Agents, Cloudflare Agents, Flowise, LLM Spark.
Everything these tools do, in one app
- Visual agent builder Lets users assemble agents and workflows by dragging and dropping components on a canvas.Found in Sim, Agent Development Kit, Flowise
- Code-based agent building Allows developers to define agents and workflows directly in code for full control.Found in Open Agent Kit, VoltAgent, Sim
- Multi-turn conversations Enables agents to maintain context across multiple exchanges with users.Found in Open Agent Kit
- LLM integration Connects agents to various large language models for natural language understanding and generation.Found in Open Agent Kit, VoltAgent, Sim and 3 more
- External tool integration Lets agents call external services, APIs, and tools to perform actions.Found in Sim, Zen Agents (by Zencoder), Retool Agents and 1 more
- Multi-agent orchestration Coordinates multiple agents to work together on complex workflows.Found in VoltAgent
- Agent workflow management Provides tools to define, manage, and execute agent decision-making processes and workflows.Found in Open Agent Kit, Sim, Flowise
- Observability and debugging Offers visibility into agent execution steps, traces, and logs for troubleshooting and optimization.Found in VoltAgent, Flowise, LLM Spark
- Testing and evaluation Includes tools to test prompts, debug agents, and evaluate their performance.Found in Agent Development Kit, Retool Agents, LLM Spark
- Human-in-the-loop Allows humans to review and approve agent actions before they are executed.Found in Flowise
- State persistence Enables agents to maintain memory and context over time across interactions.Found in Cloudflare Agents
- Real-time communication Supports real-time interaction between agents and users via WebSockets.Found in Cloudflare Agents
- Shared workspace context Keeps workflows, data tables, knowledge bases, and files in one place so agents share memory and data.Found in Sim
- Deterministic steps Replaces some LLM calls with deterministic code to reduce token usage and cost.Found in Sim
- Self-hosting Allows the platform to be run on the user's own infrastructure.Found in Sim
- Organization-wide sharing Lets teams share custom agents across the organization to standardize practices.Found in Zen Agents (by Zencoder)
- Marketplace Provides a community-driven repository of pre-built agents and components.Found in Zen Agents (by Zencoder)
- Version control Tracks changes to prompts and projects, supporting collaboration and rollback.Found in LLM Spark
- Team collaboration Facilitates real-time collaboration among team members on agent development.Found in LLM Spark
- Instant deployment Streamlines moving agents from development to production with fast deployment.Found in LLM Spark
How it works, step by step
- Assemble agents and workflows by dragging components on a canvas
- Define agents and workflows directly in code
- Maintain context across multi-turn conversations
- Connect agents to selected large language models
- Let agents call external services, APIs and tools
- Coordinate multiple agents on one workflow
- Define and execute agent decision-making steps
- Show execution traces, logs and step-level debugging
- Test prompts and evaluate agent performance on held-out cases
- Require human review and approval before consequential actions
- Persist agent memory and context across interactions
- Support real-time interaction over WebSockets
- Keep workflows, data tables, knowledge bases and files in one shared workspace
- Replace selected LLM calls with deterministic code to cut token use
- Run the platform on the buyer's own infrastructure
- Share approved agents across the organization
- Offer a repository of pre-built agents and components
- Track prompt and project changes with rollback
- Support real-time team collaboration on agent development
- Move agents from development to production with fast deployment
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Conversational agent build and operations workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Conversational agent build and operations workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data200 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce the number of rented agent tools and keep agent definitions, conversations, traces and approvals in one owned workspace. For product and platform teams building and running conversational AI agents, convert approved prompts, tool definitions, model settings, workflow steps and review rules into a deployed, monitored agent with human approval points. The benefit is a testable hypothesis, measured through accepted agent tasks per delivery hour and production incidents per released agent; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect approved prompts, tool definitions, model settings, workflow steps and review rules, then follow this sequence: 1. Assemble agents and workflows by dragging components on a canvas. 2. Define agents and workflows directly in code. 3. Maintain context across multi-turn conversations. 4. Connect agents to selected large language models. 5. Let agents call external services, APIs and tools. 6. Coordinate multiple agents on one workflow. 7. Define and execute agent decision-making steps. 8. Show execution traces, logs and step-level debugging. 9. Test prompts and evaluate agent performance on held-out cases. 10. Require human review and approval before consequential actions. Resolve uncertain cases with qualified reviewers, approve a deployed, monitored agent with human approval points, and measure accepted agent tasks per delivery hour and production incidents per released agent against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate agent definitions and workflow steps for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints, tool-call routing and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Model choice, tool permissions and approval rules remain buyer decisions; final release and consequential actions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve buyer data boundaries, source attribution, tool permissions and usage rights. Buyers approve agent release, tool access and consequential actions. One buyer team, one conversational agent and one external tool integration; final release 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.
What to build first
Pilot scope: One buyer team, one conversational agent and one external tool integration; final release and consequential actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: assemble agents and workflows by dragging components on a canvas; define agents and workflows directly in code. Support the remaining modules with operator review: maintain context across multi-turn conversations; connect agents to selected large language models; let agents call external services, APIs and tools; coordinate multiple agents on one workflow; define and execute agent decision-making steps; show execution traces, logs and step-level debugging; test prompts and evaluate agent performance on held-out cases; require human review and approval before consequential actions. 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.
What it can connect to
Buyer-owned prompts, tool definitions and knowledge sources. Cloud or self-hosted model endpoints, external APIs, WebSocket channels 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.
The screens in detail
Primary screens: Agent and workflow builder, Test and evaluation bench, Deployment and operations console. Use a project list with environment badges, a central canvas or code editor for agent definitions, and a right-hand panel for models, tools, memory and review rules. Let users compare prompt and workflow versions side by side. Display draft, in review, approved and live states. Provide a trace viewer with step-by-step execution, tool calls and human approval records. Make the task-specific outcome a deployed, monitored agent with human approval points visible beside its evidence, review state and value baseline.
