How a database subsetting tool turned a plain-English request into a reviewable, undoable extraction model — instead of just another SQL-generation chatbot.
Learn how to build a production-ready multi-agent AI framework in Python that improves reliability, reduces hallucinations, and scales enterprise LLM workflows.
Use Temporal for orchestration, Kafka for chunk processing, object storage for payloads, and RAG to retrieve relevant data without overwhelming clients.
Agentic clients via MCP are a new human interface to old software. They are an appealing choice: they use native language. But it is not always the wise approach.
Turn video and audio recordings into searchable, citable knowledge for Microsoft Foundry IQ using Azure Content Understanding, MarkItDown, and structured metadata.
AI agents need least-privilege permissions, scoped identities, and policy controls to safely execute actions without exceeding their intended authority.
AI agents will eventually take a destructive action your stack never planned for. Here are five ways to strengthen your identity strategy before agents find the gaps.
Spring Boot pods reload the same classes on every start. A CDS training run inside your Dockerfile caches that work once and cuts startup time roughly in half.
Learn how an early-stage open-source project separates workload lifecycle from compute allocation for bursty, stateful, and massively concurrent AI workloads.
This guide walks you through the core architecture components and design patterns needed to build scalable microservices with Node.js and explains when to use each.
Enterprise AI agents need secure execution boundaries, deterministic logic, identity, governance, and auditing—not just intelligent models—to safely act in production.