Engineering reliable AI systems.

An architectural look at the security risks introduced when AI agents gain access to tools, enterprise data, memory, and other agents—and why application-level guardrails are not enough.

An exploration of memory architectures for AI agents, including how systems can retain, retrieve, and use information across interactions without overwhelming the context window.

A modular enterprise evaluation framework combining ground-truth generation, customizable metrics, human review, continuous monitoring, and responsible AI testing.

A practical architecture for capturing real-world feedback, evaluating model behavior, and turning production signals into controlled improvements across the AI lifecycle.

A perspective on how specialized AI agents can discover capabilities, collaborate securely, and operate as part of a governed enterprise agent ecosystem.

A concise guide to the distance and similarity measures behind clustering, vector search, FAISS, HNSW, and modern information-retrieval systems.

An introduction to the practices, infrastructure, and operational disciplines required to move traditional ML and large language model applications into production.