Operations | Monitoring | ITSM | DevOps | Cloud

Most AI programs don’t fail on model quality. They fail because the experience makes people either over-trust or quietly avoid the system. Employees often use AI more than leaders realize, frequently without training or guardrails. Interfaces that just “show an answer” without confidence, provenance, or recourse create two risks: blind reliance and shadow use.

If there’s one thing clear from our AI-powered observability webinar, it’s that observability has officially graduated from a “nice-to-have” to a business-critical discipline, and AI is helping lead that charge. Our webinar brought together guest speaker Stephen Elliott, Group VP at IDC, and Ranbir Chawla, former SVP of Engineering at RB Global, for an hour of insights that mixed data, experience, and hard-won lessons from the trenches.

Engineering teams today manage infrastructure spread across multiple clouds and tools. Whether this happened through gradual accumulation or deliberate strategy, the result is the same: complexity that slows teams down. Managing each cloud separately with different tools and workflows is a bottleneck to delivery speed, operational efficiency, and platform reliability.

For most Central IT leaders, tech debt isn't a surprise. It's the silent tax on every roadmap, every quarterly plan, every conversation about why things take so long. Modern cloud application platforms (true PaaS environments) give IT leaders a path to unwind years of accumulated complexity while simultaneously accelerating innovation. You no longer have to tolerate the tax.

Asset management software is becoming the backbone of modern organizations in 2026. With rising demand for automation global compliance pressure and the need for accurate real time visibility companies are rapidly shifting from outdated manual processes to intelligent digital asset management solutions.

You've set up the Cortex MCP and connected it to your AI assistant and IDE. You ask about service ownership, check a Scorecard or two, and it works. You're impressed by how much faster this is than clicking through the web UI. Now you're wondering what else you can do with it. I'm willing to bet we've hit a nerve with that "hypothetical" scenario. The Cortex MCP works exactly as designed, but it's deceptively difficult to know which questions to ask and when to ask them.

HTTP/1.1 is a text-based protocol where the message framing is mixed with its semantics, making it easy to parse incorrectly. The boundaries between messages are very weak because there is no clear delimiter between them. Thus, HTTP/1.1 parsers are especially vulnerable to request smuggling attacks.

Organizations are struggling with rising operational complexity, fragmented tools, and inconsistent security enforcement as Kubernetes becomes the foundation for modern application platforms. As a result of this complexity and fragmentation, platform teams are increasingly burdened by the need to stitch together separate solutions for networking, network security, and observability.

We launched agent monitoring earlier this year, allowing our users to instrument LLM usage and tool calls in their applications. However, we only had Agent Monitoring support for Python and JavaScript. We’ve been working on creating an Agent Monitoring SDK for.NET — specifically for Microsoft.Extensions.AI.Abstractions.

For our last episode of 2025, we’re focusing on Datadog releases announced at AWS re:Invent. Join Jeremy to see how you can manage logs at petabyte scale in your infrastructure, eliminate unneeded costs in Amazon S3 buckets, build agentic workflows, and detect credential leaks. Later in the episode, Scott spotlights how you can connect your AI agents to Datadog tools and context with our MCP Server.