AI app for it and development · no coding needed
Code-first agent workflow runtime console
Reduce the number of rented tools and give the team one owned runtime for defining, running, inspecting and approving agent workflows.
Made for: Engineering teams building and running AI agent workflows that use tools to complete multi-step tasks
What it does for you
The problem
Agent workflows are spread across separate tools for definition, execution, memory, scheduling, permissions, grading and observability, so teams rent several subscriptions and still cannot see or control a run end to end.
What it gives you
A source-linked assistant and administrator console
What you give it
Workflow source filestoolAPI credentialsmemory storesrubricspermission scopes
Build your own version of OpenMolt, AgentLoop and more
One app with what these 3 AI tools do, yours to keep and change: OpenMolt, AgentLoop, Finyuus.
Everything these tools do, in one app
- Code-first workflow definitions Define agents and workflows directly in code or a text-based DSL within your codebase.Found in OpenMolt, Finyuus
- Planning and execution loop Agents plan steps and execute tools to complete multi-step tasks with retries and conditional flows.Found in OpenMolt
- Durable execution Workflows run with retries, cancellation, and replayability backed by a durable runtime.Found in Finyuus
- Memory persistence Short-term and long-term memory with persistence callbacks carry state across sessions.Found in OpenMolt
- File-based memory State is carried across clean contexts using files.Found in AgentLoop
- Tool and API integrations Connect agents to external tools and APIs.Found in OpenMolt
- Scheduling Schedule agent runs.Found in OpenMolt
- CLI runner Run agents from the command line in different contexts.Found in OpenMolt
- Capability-based permissions Restrict agent access to explicitly allowed tools and scopes.Found in OpenMolt
- Fresh worker and critic per cycle Each cycle starts new Codex sessions for worker and critic so no prior conversation carries over.Found in AgentLoop
- Rubric-based grading Define pass/fail criteria in a markdown file that the critic grades against and produces fix notes.Found in AgentLoop
- Live dashboard A local web interface shows cycle progress, logs, and state, and allows cancelling a run mid-cycle.Found in AgentLoop, Finyuus
- MCP bridge Allows ChatGPT to send goals to the daemon and monitor runs without exposing the daemon publicly.Found in AgentLoop
- Polish mode After a PASS, leftover cycles switch the critic to an open-ended improve-or-ship question for refinements beyond the rubric.Found in AgentLoop
- Network disabled in project folder Runs are contained by disabling network access inside the project folder.Found in AgentLoop
- Human approvals Workflows can pause for human approval at defined points.Found in Finyuus
- Static analyzability Parse a workflow to see which tools it calls, which guards it runs, and where it pauses for human approval, without executing it.Found in Finyuus
- Integrated observability Tracing and cost reporting are wired in via Langfuse.Found in Finyuus
- Separation of AI logic from application code The DSL cannot import or call into the host application, enforcing separation.Found in Finyuus
- Git-diffable text workflows Workflows are stored as plain text files, giving Git history, PRs, and readable diffs.Found in Finyuus
How it works, step by step
- Define agents and workflows in code or a text-based DSL inside the codebase
- Plan steps and execute tools with retries and conditional flows
- Run workflows on a durable runtime with retries, cancellation and replay
- Persist short-term and long-term memory with callbacks across sessions
- Carry state across clean contexts using files
- Connect agents to external tools and APIs
- Schedule agent runs
- Run agents from the command line in different contexts
- Restrict agent access to explicitly allowed tools and scopes
- Start a fresh worker and critic session each cycle so no prior conversation carries over
- Grade against a markdown rubric and produce fix notes
- Show cycle progress, logs and state in a local web dashboard and allow cancelling a run mid-cycle
- Bridge ChatGPT to send goals to the daemon and monitor runs without exposing the daemon publicly
- Switch the critic to an open-ended improve-or-ship question after a PASS
- Disable network access inside the project folder during runs
- Pause workflows for human approval at defined points
- Parse a workflow to list tools, guards and approval pauses without executing it
- Wire in tracing and cost reporting through Langfuse
- Enforce separation so the DSL cannot import or call into the host application
- Store workflows as plain text files for Git history, PRs and readable diffs
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 Code-first agent workflow runtime console 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 Code-first agent workflow runtime console 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 Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data195 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 tools and give the team one owned runtime for defining, running, inspecting and approving agent workflows. For engineering teams building and running AI agent workflows that use tools to complete multi-step tasks, convert code-first workflow definitions, tool and API connections, memory stores, schedules, permissions, rubrics and run traces into a source-linked assistant and administrator console. The benefit is a testable hypothesis, measured through completed multi-step tasks per engineering hour and rework after a run is accepted; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect workflow source files, tool and API credentials, memory stores, rubrics and permission scopes, then follow this sequence: 1. Define agents and workflows in code or a text-based DSL inside the codebase. 2. Plan steps and execute tools with retries and conditional flows. 3. Run workflows on a durable runtime with retries, cancellation and replay. Resolve uncertain cases with qualified reviewers, approve a source-linked assistant and administrator console, and measure completed multi-step tasks per engineering hour and rework after a run is accepted against a documented baseline.
How the AI works
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 runtime version and approved tool set; final code review and release decisions remain engineering. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve code ownership, source attribution, credential handling and usage permissions. Engineering owners approve substantive changes and deployment scope. One fixed runtime version and approved tool set; final code review and release 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 version and approved tool set; final code review and release decisions remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: define agents and workflows in code or a text-based DSL inside the codebase; plan steps and execute tools with retries and conditional flows. Support the third module with operator review: run workflows on a durable runtime with retries, cancellation and replay. 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
Team-owned repositories, authorized tool and API endpoints and permitted model providers. Cloud run storage, Git hosting and observability 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.
The screens in detail
Primary screens: Workflow source and definitions, Live run dashboard, Review and approval queue. Use a file tree for workflow text files, a central editor with diff view, and a right-hand panel for tools, guards, memory and approval points. Let users compare run versions side by side. Display queued, running, paused, passed and failed states. Provide a client preview link with comments anchored to the relevant run step. Make the task-specific outcome a source-linked assistant and administrator console visible beside its evidence, review state and value baseline.
