AI app for it and development · no coding needed
Agentic coding task delivery workspace
Reduce coordination overhead while keeping code review and release authority with the team.
Made for: Engineering teams and technical leads delegating coding tasks to AI agents
What it does for you
The problem
Coding agents run in scattered tools, so plans, edits, tests, pull requests and cost records are hard to review, steer and audit.
What it gives you
Reviewed, tested code changes linked to pull requests
What you give it
Repository accessissue-tracker taskscoding conventionsreview rules
Build your own version of Claude Code on the web, happycapy and more
One app with what these 10 AI tools do, yours to keep and change: Claude Code on the web, happycapy, GitHub Copilot Workspace, VibeShift MCP, Parallax, Claude Code Desktop App Redesigned, Cline CLI 2.0, kimiflare, Lovelace, Agent Bar.
Everything these tools do, in one app
- AI coding agent An AI assistant that plans and performs coding tasks on your behalf.Found in Claude Code on the web, happycapy, GitHub Copilot Workspace and 7 more
- Code generation and edits Generates new code and makes context-aware edits to existing files.Found in Claude Code on the web, happycapy, GitHub Copilot Workspace and 6 more
- Parallel task execution Runs multiple coding tasks or agents concurrently.Found in Claude Code on the web, happycapy, Claude Code Desktop App Redesigned and 1 more
- GitHub integration Connects to GitHub repositories to work on code and changes.Found in Claude Code on the web, GitHub Copilot Workspace, Parallax and 1 more
- Automatic pull requests Creates or updates pull requests for review automatically.Found in Claude Code on the web, Parallax
- Isolated execution environments Runs each task in a contained sandbox or worktree with restricted access.Found in Claude Code on the web, happycapy, Parallax
- Real-time progress tracking Lets you monitor and steer tasks while they run.Found in Claude Code on the web, Agent Bar
- Browser and mobile access Use the tool from a web browser or mobile device.Found in Claude Code on the web, happycapy, Lovelace
- Terminal-native workflow Operates directly from the command line.Found in Cline CLI 2.0, kimiflare
- Plan-first approval Generates a change plan and waits for your approval before editing.Found in Parallax, GitHub Copilot Workspace
- Issue tracker integration Pulls tasks from issue trackers like Linear or GitHub Issues.Found in Parallax
- Local-first execution Runs code changes and agent behavior on your own machine.Found in Parallax
- Integrated editor and terminal Review diffs, edit files, and run commands in one interface.Found in Claude Code Desktop App Redesigned, GitHub Copilot Workspace
- Session management Organizes and filters multiple agent sessions in one place.Found in Claude Code Desktop App Redesigned, Claude Code on the web
- Headless CI/CD mode Pipes agent actions into automated build and deployment pipelines.Found in Cline CLI 2.0
- Editor protocol integration Connects external editors and tools via a standard protocol.Found in Cline CLI 2.0
- Large-context conversations Maintains long multi-turn coding sessions with a large context window.Found in kimiflare
- Voice input Dictate prompts instead of typing them.Found in Agent Bar
- Cost tracking Tracks token and dollar usage per session.Found in Agent Bar
How it works, step by step
- Plan coding tasks from repository and issue context
- Generate new code and context-aware edits
- Run multiple coding tasks or agents concurrently
- Connect to GitHub repositories and branches
- Open or update pull requests automatically
- Run each task in an isolated sandbox or worktree
- Track and steer running tasks in real time
- Provide browser and mobile access
- Support terminal-native operation
- Require plan approval before editing
- Pull tasks from issue trackers
- Support local-first execution on the team's machines
- Review diffs, edit files and run commands in one interface
- Organize and filter multiple agent sessions
- Pipe agent actions into CI/CD pipelines
- Connect external editors via a standard protocol
- Maintain long multi-turn coding sessions
- Accept dictated prompts
- Track token and dollar usage per session
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before merge
- Export a versioned reviewed, tested code changes linked to pull requests 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 Agentic coding task delivery 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 Agentic coding task delivery 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 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 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 coordination overhead while keeping code review and release authority with the team. For engineering teams and technical leads delegating coding tasks to AI agents, convert repository access, issue-tracker tasks, coding conventions and review rules into reviewed, tested code changes linked to pull requests. The benefit is a testable hypothesis, measured through accepted pull requests per engineering hour and corrections after merge; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect repository access, issue-tracker tasks, coding conventions and review rules, then follow this sequence: 1. Plan coding tasks from repository and issue context. 2. Generate new code and context-aware edits. 3. Run each task in an isolated sandbox or worktree. 4. Require plan approval before editing. 5. Open or update pull requests automatically. Resolve uncertain cases with qualified reviewers, approve reviewed, tested code changes linked to pull requests, and measure accepted pull requests per engineering hour and corrections after merge 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 repository host and one CI provider; final code review, security checks and release decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve code ownership, source attribution, license accuracy and usage permissions. Engineering leads approve substantive changes and release scope. One repository host and one CI provider; final code review, security checks and release decisions 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 repository host and one CI provider; final code review, security checks and release decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: plan coding tasks from repository and issue context; generate new code and context-aware edits. Support the third module with operator review: run each task in an isolated sandbox or worktree. 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 issue trackers and permitted CI/CD pipelines. Cloud code storage, editor protocol connections and deployment 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: Task intake and plan approval, Agent run and diff review, Pull request and delivery. Use a session list for repositories and tasks, a large central diff and terminal view, and a right-hand panel for plan, tests, cost and comments. Let users compare agent runs side by side. Display planned, running, changes requested and merged states. Provide a review link with comments anchored to the relevant file and line. Make the task-specific outcome reviewed, tested code changes linked to pull requests visible beside its evidence, review state and value baseline.
