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.
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.
Learn how an early-stage open-source project separates workload lifecycle from compute allocation for bursty, stateful, and massively concurrent AI workloads.
Learn best practices for handling bad data in stream processing, from schema validation and duplicate detection to dead-letter queues, monitoring, and data lineage.
Fetch AQI data from IQAir, store it in Neo4j, then visualize it with pydeck, Leaflet and R, plus Cypher queries showing what graph-native analysis looks like.
Enterprise AI agents need secure execution boundaries, deterministic logic, identity, governance, and auditing—not just intelligent models—to safely act in production.
EA tools centralize business and IT data to improve alignment, governance, decision-making, and portfolio management while enabling AI-driven automation.