AI-OS: Autonomous Multi-Agent Orchestration
View on GitHubA next-generation AI Operating System architecture featuring autonomous multi-agent collaboration, dynamic DAG planning, and self-healing execution loops.
Category
AI/ML
Role
Lead AI Engineer
Timeline
6 Months
Tech Stack
AI-OS: Autonomous Multi-Agent Orchestration
System Architecture
A high-level overview of the technical components and data flow that power AI-OS: Autonomous Multi-Agent Orchestration.
The Approach
Architected a reinforcement learning (RL) powered protein folding platform that leverages deep learning models to search the vast protein structure space for novel sequences matching user-specified biochemical functions. Built a FastAPI backend to orchestrate model inference, training pipeline execution, and 3D structure visualization data streaming. Integrated AlphaFold2-derived model architectures with custom reward functions that optimize for stability, binding affinity, and target function fulfillment. The React-based frontend provides interactive 3D structure rendering using Three.js, real-time training metric tracking, and experimental result comparison tools to help researchers iterate on protein designs efficiently.
Key Challenges
- Optimizing RL training stability to prevent reward collapse during long training runs on complex biochemical target functions.
- Reducing inference latency for protein structure prediction while maintaining model accuracy across diverse protein families.
- Implementing distributed training across GPU clusters that efficiently handles large batch sizes and model checkpointing.
- Creating an intuitive 3D visualization layer that renders high-resolution protein structures without sacrificing browser performance.