AI app for it and development · no coding needed
Shared human-agent workspace coordination portal
Give teams and AI agents one shared workspace with visible handoffs and approval gates.
Made for: Engineering and operations teams that run AI agents alongside people on shared projects
What it does for you
The problem
Teams and AI agents work in separate tools, so handoffs, decisions and approvals are invisible and agents act without human control.
What it gives you
A permissioned workspace where humans and agents own work, review results and hand off tasks
What you give it
Team conversationsagent configurationsconnected accountstask history
Build your own version of Offloop, Spaces and more
One app with what these 5 AI tools do, yours to keep and change: Offloop, Spaces, Offsite, Inline, AgentOS.
Everything these tools do, in one app
- Shared team workspace A single place where team members and AI agents work together on projects.Found in Offloop, Spaces, Offsite and 2 more
- AI agents as participants AI agents join the workspace as first-class participants who can own work, review results, and hand off tasks.Found in Offloop, Offsite, Inline
- Visible conversations and decisions Keeps team conversations and decisions visible to everyone in the workspace.Found in Offloop, Spaces, Offsite and 1 more
- Human approval gates Requires human approval before agents take real-world actions, showing full conversational lineage.Found in Offloop, Offsite, AgentOS
- Bring your own model Lets users connect their own LLM provider accounts or local models without the tool reselling tokens.Found in Offloop, Spaces
- Per-agent model selection Allows choosing a specific model for each agent or setting a workspace default.Found in Offloop, AgentOS
- Scheduled routines Runs recurring tasks on a schedule and notifies a person only when a decision is needed.Found in Offloop, Spaces
- Reusable agents and flows Successful agents and flows can be reused across retries, waits, schedules, and handoffs.Found in Offloop, AgentOS
- Local credential storage Stores agent configurations, API keys, and connected accounts locally on each person's computer.Found in Spaces
- Configurable AI specialists Creates AI specialists like researcher or copywriter, each with dedicated instructions, memory, and tools.Found in Spaces
- Live org-chart view Shows humans and agents as nodes on a live org chart with real-time conversation flows and clickable traces.Found in Offsite
- Event-driven agent wake Uses an event-driven wake/inbox model to avoid constant compute usage while keeping teams ready.Found in Offsite
- MCP-compatible agents Supports out-of-the-box integrations and MCP-compatible agents that can be spun up with memory and guardrails.Found in Offsite
- Thread-based messaging Separates agent output from human conversation using threaded messages.Found in Inline
- Centralized agent management Organizes projects, roles, and shared context for multiple agents from one control surface.Found in AgentOS
- Task and job orchestration Manages tasks and jobs with session history, task-level execution details, and approval workflows.Found in AgentOS
- Runtime visibility Provides model selection, activity logs, and token/usage tracking per task or agent.Found in AgentOS
- Local-first open-source Supports customization and self-hosting with a local-first, open-source architecture.Found in AgentOS
How it works, step by step
- Create one shared workspace for people and agents
- Add AI agents as first-class participants who own work and hand off tasks
- Keep conversations and decisions visible to everyone
- Require human approval before agents take real-world actions
- Connect your own LLM provider accounts or local models
- Choose a model per agent or set a workspace default
- Run recurring tasks on a schedule and notify a person only when a decision is needed
- Reuse successful agents and flows across retries, waits, schedules and handoffs
- Store agent configurations, API keys and connected accounts locally on each computer
- Create configurable AI specialists with dedicated instructions, memory and tools
- Show humans and agents as nodes on a live org chart with clickable traces
- Wake agents on events instead of constant compute
- Support MCP-compatible agents and out-of-the-box integrations
- Separate agent output from human conversation using threaded messages
- Manage projects, roles and shared context for multiple agents from one control surface
- Orchestrate tasks and jobs with session history, task-level execution details and approval workflows
- Show model selection, activity logs and token or usage tracking per task or agent
- Support local-first, open-source customization and self-hosting
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 Shared human-agent workspace coordination 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 Shared human-agent workspace coordination 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
Give teams and AI agents one shared workspace with visible handoffs and approval gates. For engineering and operations teams that run AI agents alongside people on shared projects, convert team conversations, agent configurations, connected accounts and task history into a permissioned workspace where humans and agents own work, review results and hand off tasks. The benefit is a testable hypothesis, measured through approved handoffs per week and unapproved agent actions; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect team conversations, agent configurations, connected accounts and task history, then follow this sequence: 1. Create one shared workspace for people and agents. 2. Add AI agents as first-class participants who own work and hand off tasks. 3. Keep conversations and decisions visible to everyone. 4. Require human approval before agents take real-world actions. Resolve uncertain cases with qualified reviewers, approve a permissioned workspace where humans and agents own work, review results and hand off tasks, and measure approved handoffs per week and unapproved agent actions 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 workspace with a fixed set of connected accounts and approved models; final approval and real-world actions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve team voice, source attribution, decision accuracy and usage permissions. Named owners approve substantive changes and external actions. One workspace with a fixed set of connected accounts and approved models; final approval and real-world 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 workspace with a fixed set of connected accounts and approved models; final approval and real-world actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: create one shared workspace for people and agents; add AI agents as first-class participants who own work and hand off tasks. Support the third module with operator review: keep conversations and decisions visible to everyone. 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 task systems and permitted communication sources. Cloud storage, identity providers 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: Workspace overview and org chart, Agent and flow configuration, Task and approval queue. Use a live org-chart view with humans and agents as nodes, a central task board with threaded conversations, and a right-hand panel for agent instructions, model choice and runtime logs. Let users compare agent versions side by side. Display draft, awaiting approval and approved states. Provide a client preview link with comments anchored to the relevant task. Make the task-specific outcome a permissioned workspace where humans and agents own work, review results and hand off tasks visible beside its evidence, review state and value baseline.
