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.
Apache Spark job performance issues are frequently caused by improper join strategies leading to excessive data shuffling, rather than suboptimal code.
Build a safer Python API client with timeouts, selective retries, exponential backoff, jitter, and better handling of rate limits and temporary failures.
In Part 3 of this casual, jargon-free series, we break down hashing, salting, rainbow table attacks, and asymmetric encryption (RSA) — all with everyday analogies.
Learn how an early-stage open-source project separates workload lifecycle from compute allocation for bursty, stateful, and massively concurrent AI workloads.