ORCHESTRA — Agentic AI Platform

Inspiration

Modern workflows involve multiple disconnected tools such as calendars, email, knowledge bases, and productivity applications. Users often need to switch between different interfaces and manually coordinate actions. We wanted to build ORCHESTRA, an agentic AI platform that helps coordinate tools and workflows through a unified interface, with human approval for consequential actions.

What it does

ORCHESTRA provides a unified workspace for managing missions, knowledge, integrations, activity, and tools. Its orchestration layer coordinates workflow execution, while its tool integration architecture provides a foundation for connecting external services. Security controls help prevent consequential actions from being executed without the required approval.

The project includes a web interface, an orchestration package, an MCP server, and an MCP client. It also includes persistent SQLite storage and configurable authentication for protected API operations.

Live demo: https://alexa-orchestra.onrender.com

Source code: https://github.com/25A31A4325/Alexa-

How we built it

We built ORCHESTRA as a TypeScript monorepo with separate packages for orchestration, MCP client functionality, MCP server functionality, and the web application. We used Node.js for the server runtime, SQLite for persistent storage, and a web-based interface for interacting with the platform.

We implemented API authentication, approval fingerprinting using SHA-256, health endpoints, and automated tests. The application is deployed on Render for public demonstration.

Challenges we ran into

Key challenges included coordinating multiple packages, maintaining reliable workflow execution, preserving state across process restarts, and enforcing security boundaries around consequential actions. We addressed these challenges through automated testing, configurable service settings, persistent storage, and explicit approval checks.

Accomplishments that we're proud of

We built ORCHESTRA with a modular architecture, MCP integration, SQLite persistence, API authentication, approval safeguards, and automated tests.

What we learned

We learned about agentic AI, MCP, TypeScript architecture, API security, database persistence, automated testing, and cloud deployment.

What's next for ORCHESTRA — Agentic AI Platform

We plan to expand real-world integrations, improve Google OAuth connectivity, strengthen security, and make workflow automation more reliable and user-friendly.