Operations | Monitoring | ITSM | DevOps | Cloud

October’s This Month in Datadog comes to you from the Datadog Summit in San Francisco, where Jeremy shares a look inside the event, with our latest product updates. Plus, we cover how to: Build and manage Datadog Workflows using natural language, turning descriptions of automation tasks into workflows that teams can review before publishing Detect and investigate Kafka issues with Kafka Console, bringing health and performance data into one place Turn raw logs into structured fields with Datadog Tap, making logs easier to search and analyze.

The timeline between vulnerability disclosure and active weaponization has collapsed. Threat actors no longer wait weeks or months—in many cases, security and IT teams have 72 hours or less to identify exposure, prioritize risk, and deploy countermeasures before exploitation begins.

In the October edition of the Kubernetes Monitoring Helm chart office hours, we discuss the version 4.6 release and the upcoming 4.7 feature release, and the next 5.0 major release.

We asked some members of the Grafana AI team what AI tool they use every day, and here are the results. This was taken in June 2026, at the AI offsite in Amsterdam, the Netherlands.

Custom flows can run anything a repo puts in front of them. This week you decide which ones your organization accepts. Everything that shipped in env zero from v4.0.287 to v4.0.298, plus self-hosted agent 5.5.10 and 5.5.11. In this week's release: New EZ Release Weekly every Friday.

Bryan Peace Leon Adato break down what exactly security-minded IT folks - whether they work in SRE, dev, networking, or more esoteric disciplines - should do when a new, critical, apocalyptic vulnerability is announced. They walk you step by step through research, analysis, and how to protect your company if a patch isn't out yet. Along the way they muse about the nature of the industry, the sudden spike in CVEs, and where to find the best hard cidr to take the edge off.

Running an agent in your cluster should not mean opening it to the internet. AURA reaches Slack over an outbound socket, so the cluster stays locked down. Jeff Vogt connects an AURA deployment running in Kubernetes to Slack using AURA's direct Slack integration. The setup is a Slack app with an app token and a bot token, plus a few lines added to the deployment's configuration. The Kubernetes MCP server is already deployed and connected to AURA before the video starts.

One of the most expensive failure modes in AI agents is a silent retry loop. An agent hits a rate limit, blindly retries until it succeeds, the user gets a response, and no ticket ever gets filed. Under the hood, tokens are burning, latency is compounding, and a transient error today becomes a sustained outage tomorrow.

Most teams keep an agent from regressing by hardcoding checks into its logic, an if-statement here, a regex there. Every new rule then becomes a code change, a review, and a deploy, and the person who spots the problem in production is rarely the person who can ship the fix. Agent Control moves those rules out of the code and into one hub. Steer, block, and validate agent behavior in real time, with rules any team member can update without touching the codebase.