About the Project
This project presents an end-to-end framework for deploying AI agents in a high-stakes semiconductor manufacturing environment. The core innovation is a "Glass Box" architecture built on LangGraph that provides full observability and deterministic, state-aware orchestration from sensor data intake to role-based output.
The system addresses critical enterprise pain points identified through stakeholder interviews: static data integration, manual root cause analysis consuming 40% of engineering time, and "black box" AI that lacks the audit trails required for Configuration Control Board (CCB) approval. Our solution introduces a phased enterprise roadmap progressing from Shadow mode (recommend-only) through Supervised (human-in-the-loop) to Autonomous execution for certified tasks.
Key Features
- LangGraph State Management: A deterministic, state-aware pipeline orchestrating the full anomaly lifecycle from sensor to action.
- Hybrid Detection Engine: Combines Z-Score statistical analysis with Isolation Forest ML for robust anomaly identification.
- Multi-Source Correlation: Cross-references handler logs, equipment alarms, and sensor data for accurate root cause analysis.
- Controlled Execution Modes: Shadow (log only), Supervised (human review), and Autonomous (CCB-approved) modes for safe production deployment.
- Role-Based Output: Tailored responses for Process Engineers, Product Engineers, and Maintenance Technicians.
- Real-Time Data Quality Gates: Kafka/MQTT integration with validation filters to ensure clean data ingestion.
- Full Observability: LangSmith tracing for complete audit trails meeting compliance requirements.
- Enterprise Roadmap: Phased approach from Production Foundation to LLM Intelligence to Full Factory Integration.
Tech Stack
Skills
Gallery
