Build an agentic sentry that monitors New York-based enterprise cloud infrastructure for real-time vulnerabilities and performance anomalies. The system features a "Triage Agent" that identifies incident root causes and an "Engineer Agent" that autonomously drafts and tests Terraform patches in a sandbox. It bridges the gap between Generative AI and Infrastructure-as-Code (IaC), showcasing how autonomous agents can significantly reduce mean-time-to-resolution (MTTR) while maintaining strict security guardrails.

Designed for the New York life sciences sector, this project develops a researcher crew that automates the ingestion of thousands of clinical trial papers and patent filings. Using Multi-Modal RAG, agents extract complex chemical structures from images and correlate them with textual efficacy data. It demonstrates the use of Hierarchical Planning to identify "innovation gaps" in drug development, teaching participants how to handle high-fidelity data synthesis in a highly regulated industry.

This project focuses on a "Chief AI Compliance Officer" agent that monitors internal communications and financial transactions for policy violations. It utilizes Reflection Patterns to double-check its own reasoning against corporate bylaws and SEC mandates before flagging an incident. Participants learn to implement Human-in-the-loop (HITL) triggers for high-stakes ethical decisions, preparing them for leadership roles in AI Governance and Corporate Risk Management in Manhattan’s top firms.