Full-stack app and agent delivery workspace

Reduce tool stitching and rework while keeping the code and runtime under the team's control. For product teams and agencies building full-stack web applications and AI agents for their own clients, convert a written brief, data model and integration list into an exported, dockerized project with a traced agent runtime. The benefit is a testable hypothesis, measured through accepted deployments per delivery hour and post-deploy defect rate; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect the written brief, data model and integration list, then follow this sequence: 1. Capture the brief, data model and integration list. 2. Generate frontend and backend components. 3. Produce clean, maintainable code ready for deployment. 4. Keep full ownership of generated code and application. 5. Export the whole project, frontend, backend and database, fully dockerized. 6. Integrate any external service or AI tool without vendor lock-in. 7. Suggest business concepts from the brief and market notes. 8. Fill business plan templates. 9. Suggest marketing strategies aligned with the business model. 10. Let team members share and refine plans together. 11. Export plans for sharing and presentation. 12. Package memory, sandboxed tool execution and observability into every deployment. 13. Support Claude SDK, OpenAI SDK, LangGraph and CrewAI without extra glue code. 14. Mix JavaScript and Python agents or functions in one project, each agent in one language. 15. Deploy through CLI, GitHub import and CI/CD. 16. Auto-instrument LLM calls and tool invocations in cloud and local panels. Resolve uncertain cases with qualified reviewers, approve the exported, dockerized project with a traced agent runtime, and measure accepted deployments per delivery hour and post-deploy defect rate 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 target stack and one agent framework per project; security review and production sign-off remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve code ownership, source attribution, licence accuracy and usage permissions. The buyer approves substantive changes and deployment scope. One target stack and one agent framework per project; security review and production sign-off 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 target stack and one agent framework per project; security review and production sign-off remain human. Implement one approved input format, a bounded representative case set and the first two task modules: capture the brief, data model and integration list; generate frontend and backend components. Support the remaining modules with operator review: produce clean, maintainable code ready for deployment; keep full ownership of generated code and application; export the whole project, frontend, backend and database, fully dockerized; integrate any external service or AI tool without vendor lock-in; suggest business concepts from the brief and market notes; fill business plan templates; suggest marketing strategies aligned with the business model; let team members share and refine plans together; export plans for sharing and presentation; package memory, sandboxed tool execution and observability into every deployment; support Claude SDK, OpenAI SDK, LangGraph and CrewAI without extra glue code; mix JavaScript and Python agents or functions in one project, each agent in one language; deploy through CLI, GitHub import and CI/CD; auto-instrument LLM calls and tool invocations in cloud and local panels. 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, GitHub, CI/CD pipelines, cloud hosting and the buyer's chosen AI SDKs. 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: Brief and stack setup, Generated project workspace, Agent runtime and traces, Client delivery and export. Use a project list with build status, a central code and file tree view, and a right-hand panel for integrations, environment variables and review comments. Let users compare generated versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant file or trace. Make the task-specific outcome an exported, dockerized project with a traced agent runtime visible beside its evidence, review state and value baseline.