AI app for it and development · no coding needed
Agentic software delivery control workspace
Consolidate planning, coding, review and release checks into one owned workspace.
Made for: Engineering leads and platform teams shipping code changes under review and compliance rules
What it does for you
The problem
Delivery work is split across separate AI coding, ticketing, review and monitoring tools, so context, approvals and audit evidence are scattered.
What it gives you
Reviewed, approved code changes linked to tickets and release evidence
What you give it
Repository contextticketspoliciesdeployment signals
Build your own version of AI-Dev, Revolte and more
One app with what these 8 AI tools do, yours to keep and change: AI-Dev, Revolte, Warp 2.0, Producta, Factory, Shortcut for Agents, Exponent, MGX.
Everything these tools do, in one app
- AI code generation Generates code and suggestions to speed up writing and reduce repetitive work.Found in AI-Dev, Revolte, Warp 2.0 and 2 more
- IDE and editor integration Works inside common development environments so users can stay in their editor.Found in AI-Dev, Exponent
- Multi-language support Handles code in a range of programming languages.Found in AI-Dev
- Automated code refactoring Restructures existing code automatically to improve quality.Found in AI-Dev
- Error detection Finds coding errors and suggests fixes.Found in AI-Dev
- Real-time code collaboration Lets multiple people share and work on code together live.Found in AI-Dev
- Customizable AI models Lets teams adapt the AI to match their coding style and project needs.Found in AI-Dev
- Agentic workflow automation Runs multi-step delivery tasks like planning, coding, and checks with AI agents.Found in Revolte, Warp 2.0, Producta and 2 more
- Human approval gates Requires people to review and approve changes before merge or deployment.Found in Revolte
- Context mapping Pulls relevant files, history, and signals to give the AI useful background.Found in Revolte, Producta
- Policy enforcement Applies team rules and service-level settings to workflows.Found in Revolte
- Post-deployment monitoring Watches deployments and surfaces risks after release.Found in Revolte, Factory, MGX
- Security and compliance controls Provides security scans and compliance alignment for regulated teams.Found in Revolte
- Parallel agent sessions Runs multiple AI agents at once for different tasks.Found in Warp 2.0
- Inline AI code editing Lets users edit AI-generated code changes directly without switching tools.Found in Warp 2.0
- Agent status panel Shows progress and status of all active AI agents in one place.Found in Warp 2.0
- Rich input support Accepts commands, natural language, images, and links as input.Found in Warp 2.0
- Ticket system integration Connects to project management boards like Linear and Jira.Found in Producta
- Ticket clarification Automatically refines tickets so tasks are clearly defined.Found in Producta
- Automated pull requests Creates and submits code changes as pull requests automatically.Found in Producta, Revolte
- Multiple AI model support Uses different advanced AI models to improve solution quality.Found in Producta
- Visual workflow builder Lets users design automation sequences with a visual interface.Found in Factory
- Third-party integrations Connects with popular data sources and external services.Found in Factory, MGX
- Custom triggers and actions Lets users define what starts a workflow and what it does.Found in Factory
- Scalable infrastructure Supports projects of different sizes without rework.Found in Factory
- Task assignment to AI agents Lets teams assign work directly to AI teammates.Found in Shortcut for Agents
- Idea-to-task breakdown Turns high-level ideas into stories, sub-tasks, and acceptance criteria.Found in Shortcut for Agents
- Conversational status queries Answers questions about project status and bottlenecks in natural language.Found in Shortcut for Agents
- Custom AI agent API Allows building custom AI agents for specific team needs.Found in Shortcut for Agents
- Runs across environments Works in local development, CI pipelines, and editors without extra tooling.Found in Exponent
- Transparent agent activity Shows exactly what the AI agent is doing to build trust and control.Found in Exponent
- Broad engineering task support Handles tasks like debugging Docker, writing SQL, and incident response.Found in Exponent
- Automated task management Reduces manual effort by managing routine tasks automatically.Found in MGX
- Customizable workflows Adapts automation sequences to specific business needs.Found in MGX
- Real-time analytics Provides live reporting to monitor performance.Found in MGX
- User-friendly dashboard Offers an easy interface for navigation and control.Found in MGX
How it works, step by step
- Generate code and suggestions from repository context
- Work inside common editors and IDEs
- Handle multiple programming languages
- Refactor existing code automatically
- Detect errors and suggest fixes
- Support real-time shared code sessions
- Adapt models to team coding style and project needs
- Run multi-step agent workflows for planning, coding and checks
- Require human approval before merge or deployment
- Map relevant files, history and signals as context
- Enforce team rules and service-level settings
- Monitor deployments and surface post-release risks
- Apply security scans and compliance controls
- Run parallel agent sessions for separate tasks
- Edit AI-generated changes inline
- Show agent progress and status in one panel
- Accept commands, natural language, images and links as input
- Connect to Linear, Jira and similar boards
- Clarify tickets into defined tasks
- Open pull requests automatically
- Use multiple AI models for solution quality
- Build automation sequences visually
- Connect third-party data sources and services
- Define custom triggers and actions
- Scale across project sizes without rework
- Assign work to AI agents
- Break ideas into stories, sub-tasks and acceptance criteria
- Answer project status and bottleneck questions in natural language
- Expose an API for custom AI agents
- Run in local development, CI pipelines and editors
- Show transparent agent activity
- Support debugging, SQL and incident response tasks
- Manage routine tasks automatically
- Adapt workflows to specific business needs
- Report performance in real time
- Provide a dashboard for navigation and control
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed, approved code changes linked to tickets and release evidence 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 software delivery control 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 software delivery control 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 links6 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
Consolidate planning, coding, review and release checks into one owned workspace. For engineering leads and platform teams shipping code changes under review and compliance rules, convert repository context, tickets, policies and deployment signals into reviewed, approved code changes linked to tickets and release evidence. The benefit is a testable hypothesis, measured through accepted changes per engineering hour and escaped defects after release; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect repository context, tickets, policies and deployment signals, then follow this sequence: 1. Generate code and suggestions from repository context. 2. Run multi-step agent workflows for planning, coding and checks. 3. Require human approval before merge or deployment. Resolve uncertain cases with qualified reviewers, approve reviewed, approved code changes linked to tickets and release evidence, and measure accepted changes per engineering hour and escaped defects after release 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 layout and approved language set; final code review 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 layout and approved language set; final code review 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 layout and approved language set; final code review and release decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: generate code and suggestions from repository context; run multi-step agent workflows for planning, coding and checks. Support the third module with operator review: require human approval before merge or deployment. 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 repositories, authorized tickets and permitted deployment sources. Cloud code storage, editor import/export and release 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: Repository and context setup, Agent session board, Review and approval queue, Release and monitoring view. Use a project list, a central agent activity canvas, and a right-hand panel for policies, tickets and evidence. Let users compare proposed diffs side by side. Display draft, changes requested and approved states. Provide a reviewer link with comments anchored to the relevant change. Make the task-specific outcome reviewed, approved code changes linked to tickets and release evidence visible beside its evidence, review state and value baseline.
