1. About this blog Want to orchestrate multi-agent conversations in Semantic Kernel with your own deterministic logic? Microsoft’s Semantic Kernel provides GroupChatManager as an extensible base class for multi-agent orchestration. For specialized scenarios like red teaming, compliance testing, or structured workflows, you can extend it with simple conditional logic that follows your exact conversation flow requirements. In this blog, I’ll show you the approach I used in Sentinex—a red teaming framework that extends GroupChatManager with straightforward if/else logic to orchestrate adversarial conversations between researcher, model, and assessor agents.

1. About this blog Want to see how multi-agent systems can automate LLM security testing? I built Sentinex—an automated red teaming framework using .NET 8 and Microsoft Semantic Kernel. It orchestrates three AI agents (Researcher, Model Under Test, and Assessor) to systematically probe LLMs for security vulnerabilities. The framework discovered critical issues in the gpt-oss-20b model through automated adversarial conversations. In this blog, I’ll walk through how Sentinex works and what I learned building it.

📖 The Problem: Field Reports Are Not Agent-FriendlyArchaeological field reports, historical journals, and excavation notes are invaluable—but they’re locked in PDFs and text documents. When our multi-agent AI team (from Blog 4) analyzes satellite imagery or LiDAR data, they need historical context: “Has this site been previously surveyed?” “What artifacts were found in similar geological formations?” “What do historical texts say about settlements in this region?” Traditional keyword search fails here.

1. About this blog How do you transform raw LiDAR point clouds into archaeological discovery tools using Python and Azure Container Apps? LiDAR (Light Detection and Ranging) datasets contain millions of 3D points representing terrain surfaces. For archaeologists, these point clouds can reveal hidden structures, ancient settlements, and landscape modifications invisible to the naked eye. But processing raw LAS/LAZ files into actionable terrain models requires specialized geospatial tools and significant computational resources.

1. About this blog Are you tired of getting false alerts from your security cameras every time a cat walks by or a tree branch moves in the wind? If you find yourself in this situation, you’re not alone! 🚀. According to industry research, over 90% of security camera alerts are false positives - that’s right, 90%! This leads to alert fatigue where homeowners simply disable notifications, completely defeating the purpose of having a security system.

1. Why Satellite Imagery for Archaeological Discovery?When archaeologists analyze LiDAR point clouds, they see terrain elevation changes — subtle mounds, ditches, and depressions that might indicate ancient structures. But LiDAR alone doesn’t tell the full story. What was the environment like? What grew there? How did the landscape change over time? This is where satellite imagery becomes crucial. In Archaios, after processing LiDAR data to detect terrain anomalies, we automatically fetch satellite imagery from Google Earth Engine to analyze: