AI app for it and development · no coding needed
Multi-agent workflow orchestration and observability portal
Run and observe multi-agent workflows from one owned portal instead of stitching several rented tools.
Made for: Engineering teams building and running multi-agent AI workflows in production
What it does for you
The problem
Agent workflows are spread across separate orchestration, provider, memory and tracing tools, so teams cannot see or control a run end to end.
What it gives you
Operator-approved workflow runs with linked evidence
What you give it
Workflow definitionsprovider credentialsagent memoryrun traces
Build your own version of Compozy, TensorBlock Forge and more
One app with what these 4 AI tools do, yours to keep and change: Compozy, TensorBlock Forge, Mastra, GraphBit.
Everything these tools do, in one app
- Multi-agent orchestration Coordinate multiple AI agents to work together or compose them into larger workflows.Found in Compozy, Mastra, GraphBit
- Workflow definitions Define agent workflows in code or configuration so they can be run and reused.Found in Compozy, Mastra
- Parallel task execution Run multiple tasks or models at the same time to speed up processing.Found in Compozy, GraphBit
- Scheduling options Trigger workflows on a schedule using cron expressions or intervals.Found in Compozy
- Stateful workflow management Track and control workflow state in real time, including signal-based control.Found in Compozy
- Persistent agent memory Give agents a memory that persists across runs and can be plugged into different backends.Found in Compozy, Mastra
- Model Context Protocol support Connect agents to tools and data through the Model Context Protocol standard.Found in Compozy
- Unified multi-provider API Access multiple AI model providers through a single consistent API.Found in TensorBlock Forge
- Model switching Switch between different AI models with minimal code changes.Found in TensorBlock Forge
- Routing and failover Route requests based on priority and automatically fall back if a provider fails.Found in TensorBlock Forge
- Secure API key storage Store API keys encrypted and isolated per user for privacy.Found in TensorBlock Forge
- Local debugging UI Run, inspect, and debug workflows and agents in an interactive local interface.Found in Mastra
- Built-in tracing Trace agent and workflow execution to understand behavior and diagnose issues.Found in Mastra, GraphBit
- Evaluation primitives Evaluate agent outputs and workflows with built-in tools.Found in Mastra
- Streaming I/O Stream inputs and outputs for real-time interactions.Found in Mastra
- RAG and HITL primitives Use pre-made building blocks for retrieval-augmented generation and human-in-the-loop steps.Found in Mastra
- Low-overhead runtime Execute agents with low CPU overhead and predictable performance using an async, lock-free runtime.Found in GraphBit
- Crash resilience Keep production agents running reliably even when parts of the system fail.Found in GraphBit
- Open-source self-hosting Self-host and customize the platform without vendor lock-in.Found in Compozy, TensorBlock Forge, Mastra and 1 more
How it works, step by step
- Define agent workflows in code or configuration
- Coordinate multiple agents and compose them into larger workflows
- Run independent tasks and models in parallel
- Trigger workflows on cron schedules or intervals
- Track and control workflow state in real time, including signal-based control
- Persist agent memory across runs with pluggable backends
- Connect agents to tools and data through the Model Context Protocol
- Access multiple model providers through one consistent API
- Switch models with minimal code changes
- Route requests by priority and fall back when a provider fails
- Store API keys encrypted and isolated per user
- Run, inspect and debug workflows in a local interface
- Trace agent and workflow execution for diagnosis
- Evaluate agent outputs and workflows with built-in tools
- Stream inputs and outputs for real-time interaction
- Use pre-made RAG and human-in-the-loop building blocks
- Execute agents on a low-overhead async runtime
- Keep production agents running through partial failures
- Self-host and customize without vendor lock-in
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned operator-approved workflow run with source references and unresolved questions
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 Multi-agent workflow orchestration and observability portal 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 Multi-agent workflow orchestration and observability portal 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 links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data196 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
Run and observe multi-agent workflows from one owned portal instead of stitching several rented tools. For engineering teams building and running multi-agent AI workflows in production, convert workflow definitions, provider credentials, agent memory and run traces into operator-approved workflow runs with linked evidence. The benefit is a testable hypothesis, measured through successful workflow runs per engineering hour and mean time to diagnose a failed run; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect workflow definitions, provider credentials, agent memory and run traces, then follow this sequence: 1. Define agent workflows in code or configuration. 2. Coordinate multiple agents and compose them into larger workflows. 3. Run independent tasks and models in parallel. Resolve uncertain cases with qualified reviewers, approve operator-approved workflow runs with linked evidence, and measure successful workflow runs per engineering hour and mean time to diagnose a failed run against a documented baseline.
How the AI works
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 fixed runtime and provider set; final production deployment and incident decisions remain engineering. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, credential isolation, run reproducibility and usage permissions. Engineers approve substantive changes and deployment scope. One fixed runtime and provider set; final production deployment and incident decisions remain engineering. 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 fixed runtime and provider set; final production deployment and incident decisions remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: define agent workflows in code or configuration; coordinate multiple agents and compose them into larger workflows. Support the third module with operator review: run independent tasks and models in parallel. 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
Customer-owned workflow repositories, provider APIs and permitted data sources. Cloud runtime, secret storage, observability backends and deployment targets. 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.
The screens in detail
Primary screens: Workflow definition and run, Live run inspector, Delivery and handover. Use a list of workflows and runs, a large central run graph with step states, and a right-hand panel for logs, traces, memory and approvals. Let users compare runs side by side. Display draft, running, failed and approved states. Provide a shareable run report with comments anchored to the relevant step. Make the task-specific outcome operator-approved workflow runs with linked evidence visible beside its evidence, review state and value baseline.
