LearnFlow AI Platform
A reusable intelligence platform for agentic infrastructure using Claude Code and MCP code execution protocols.
The Challenge
AI tutors often hallucinate code solutions or lack the ability to actually run and verify the code they are teaching to students.
The Solution
Implemented the "Code Execution with MCP" pattern, allowing the AI to execute Python code in a secure sandbox and verify student submissions against real-world test cases.
System Architecture
Agentic Infrastructure
Modular skills for infrastructure deployment (Kafka, PostgreSQL, Docusaurus).
Reusable Intelligence
Teaching AI "Measurable Skills" rather than just writing boilerplate code.
Struggle Detection
Adaptive monitoring that alerts teachers when students hit specific code logic blockers.
Key Outcomes
Created a multi-agent system (Triage, Concepts, Debug, Progress agents).
Implemented token-efficient MCP Code Execution patterns (80-98% reduction).
Built reusable "Skills" that work across Claude Code and Goose agents.
Integrated Monaco Editor for real-world Python execution in a secure sandbox.