Operations | Monitoring | ITSM | DevOps | Cloud

Extending Large Language Models (LLMs) with custom tools has become increasingly valuable in today’s AI landscape. Model Context Protocol (MCP) servers provide a standardized way to connect external tools and resources to LLMs. This can enhance their capabilities beyond basic text generation. While thousands of pre-built MCP servers exist, creating your own allows you to address specific workflows. You can implement use cases that off-the-shelf solutions cannot handle.

Redis earns its place in modern stacks because it’s an in-memory data store with microsecond latency and rich data structures, making it perfect for things like caching, sessions, and rate limiting. Since it often sits on the request path, small issues (connection churn, blocked commands, memory pressure) can quickly ripple into user-visible incidents.

Over the years, Chaos Engineering has proven its effectiveness time and time again, uncovering risks and saving companies millions they would have lost in painful, brand-impacting outages. But as Chaos Engineering adoption increased, we found organizations running into the same stumbling blocks when they tried to scale. Individual teams would get great results with Chaos Engineering, then stall as they tried to get more teams involved.

In the healthcare sector, medical inventory management is far more than a simple logistical task. It directly impacts patient safety, quality of care, and cost control. A stockout of sterile gloves, syringes, or essential devices can delay a procedure, while overstocking leads to waste and ties up financial resources. With strict regulations, mandatory traceability, and urgent demands, healthcare facilities must find the right balance.

Datadog Workflow Automation can automate processes and reduce the amount of time spent on time-consuming, repetitive tasks. You can trigger these workflows in real time by tying them to alerts, dashboards, Slack messages, and other signals.