Operations | Monitoring | ITSM | DevOps | Cloud

Business teams today move fast. Developers, site reliability engineers (SREs), and product owners expect to manage incidents, changes, and requests in a way that fits naturally into how they already work with tools like Jira and Confluence. Customers expect a seamless service experience powered by automation and AI. The result is a wave of teams adopting tools like Jira Service Management to get everything they need in one place without slowing down.

My first 6 months here at PagerDuty have been a thrilling ride! PagerDuty continues to set the pace in incident management. With our 16-year track record of helping companies forge a path towards modern operations, we’ve been trusted by over 32,000 companies as the incident management platform of choice. Over these years, we’ve continuously delivered value to our customers at a rapid pace. And our customers have been vocal with us about wanting more.

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.