Project Mirror: Multi-Agent AI System for Workflow Automation

Project Mirror — Multi-Agent AI System for Workflow Automation

Project Mirror is a multi-agent AI system designed to act as a professional assistant, automating tasks such as information retrieval, scheduling, and technical reasoning through coordinated agent workflows.

The system is built to demonstrate how AI can move beyond isolated capabilities into reliable, task-oriented tools used in real workflows, where outputs directly influence decisions and actions.

Key Contributions:

• Designed a hierarchical multi-agent architecture for task decomposition and execution
• Implemented retrieval-augmented reasoning (RAG) with strict context isolation to improve accuracy
• Integrated external tools (e.g., scheduling APIs) for real-world task execution
• Improved system reliability through structured outputs and multi-layer validation
• Built observability mechanisms to track system performance and debug agent behavior

Impact:

• Reduced complex workflow execution time from hours to minutes
• Significantly improved output reliability through validation and adversarial testing
• Demonstrated a scalable approach to building production-oriented AI systems

Tech: Python, FastAPI, Google ADK, MCP, Weaviate, LLM APIs (Gemini, Llama), Docker