Operations | Monitoring | ITSM | DevOps | Cloud

As tools such as AI agents become more integrated with the instrumentation, governance, and centralization of product analytics data, product managers (PMs) still own the meaning of those events and the connected outcomes. Knowing when to trust the data, forming strong hypotheses, and being able to act on the insights requires an expert in the loop.

This guest blog post is authored by Nick Vecellio, Principal Engineer and Co-founder of NoBS, a Premier Datadog Partner specializing in hands-on Datadog migrations and optimizations. At NoBS, we help enterprises migrate their observability stack to Datadog. Teams often come to us after a migration has technically “worked,” but the new setup requires optimization tweaks to provide the clarity, reliability, or operational benefits they’re looking for.

Since the Datadog Model Context Protocol (MCP) Server first launched in Preview, Datadog has experienced an overwhelming amount of interest and feedback from customers. We appreciate those who requested access to test our product, provided feedback, and shared their stories of how the MCP Server helped them overcome engineering challenges.

When we announced Bits AI SRE at DASH 2025, we introduced an autonomous SRE agent that investigates alerts the moment they trigger. Bits AI SRE reads the same telemetry data as your team, understands your architecture, and follows your runbooks to identify likely root causes before you even open your laptop. It’s your AI teammate that’s always on call.

Modern cloud environments include thousands of resources across providers, teams, and accounts. Organizations need the ability to quickly locate the right resources so that they can manage resource compliance and troubleshoot issues. When engineers need to answer questions such as which databases are still on extended support or which storage buckets lack encryption, they often have to switch consoles, use provider-specific query languages, and know obscure version strings or configuration flags.

We recently released the 2026 State of DevSecOps study, in which we analyzed tens of thousands of applications and their respective supply chain and build system dependencies. Our research revealed trends in security posture and best practices across the software development life cycle.

Every digital experience is a chain reaction. A customer logs in to an application, an API authenticates the request, a backend call retrieves data, a page loads, and somewhere along the way, something might break. When it does, teams often chase symptoms while the root cause remains hard to find. The more distributed the system, the more difficult it becomes to see how one small failure can cascade into a visible outage.

On the first episode of This Month in Datadog in 2026, Jeremy covers how you can protect agentic AI applications with AI Guard, stay up to date and collaborate during incidents with five Incident Management releases, and ship software with confidence using Feature Flags. Later in the episode, Kevin spotlights Datadog Data Observability, which enables you to detect data quality and pipeline issues early.

Achieving end-to-end observability for applications is a top priority for organizations today, but instrumenting for both frontend and backend monitoring can be a significant hurdle. What complicates matters is that the SREs and DevOps teams responsible for deploying monitoring tools typically don’t own frontend code or have the context needed to safely modify it.

Mobile app users form opinions quickly. A slow or inconsistent startup experience can frustrate them before they reach the first screen, increasing the likelihood that they abandon the app or fail to complete key actions such as signing up or making a purchase. However, app teams often lack reliable signals that explain why startup performance varies, making it difficult to improve the user experience.