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Teams use AI coding agents and Bits Chat to troubleshoot systems and handle complex tasks, often uncovering repetitive work worth automating. But turning those routines into workflows can still require switching tools and recreating context manually. Through the Datadog MCP Server, Workflow Automation now lets you build workflows from Bits Chat or AI coding agents like Claude Code, Cursor, and Codex.

AI coding agents can generate pull requests at a pace your team has never seen. The bottleneck has shifted from writing code to everything that follows: reviewing, iterating, and merging. AI code-to-merge platforms are the category of tools built to manage that entire lifecycle, from the moment an agent starts working to the moment code lands in your main branch. This article walks through ten questions you should ask before committing to a platform.

Table of Contents A few months ago, we introduced Kubex support for the KAI Scheduler to improve GPU sharing for production inference workloads. The basic model is simple: The KAI Scheduler handles placement and GPU sharing. Kubex continuously observes usage and adjusts those allocations as demand changes. KAI provides the scheduling foundation. It lets multiple workloads share a GPU while accounting for the amount of GPU each workload requests. Kubex then closes the loop.

We are pleased to announce the release of MCP Servers 1.1.0, bringing new configuration options for HTTP-based deployments and expanded command-line capabilities for managing database connections. The new version makes it easier to control how MCP Servers are exposed over HTTP, host multiple MCP Servers under a single hostname, and configure connections directly from the command line.

NiCE Management Packs detect issues as they happen. Evaluation & Demo.

Learn how NaaS helps MSPs add connectivity services and respond faster to customers without building a global network. For many Managed Service Providers (MSPs), connectivity is still the part of a customer solution they control the least. An MSP may manage the cloud environment and secure access to it, but adding a circuit can still mean carrier lead times and manual coordination. Options may also narrow when the customer enters a new market. This reality doesn’t match with customer expectations.

Datadog’s 2026 State of AI Engineering report showed organizations’ LLM inputs swelling rapidly as context engineering expands. In March 2026, 69% of all input tokens in Datadog customer traces were for system prompts: internal instructions, policy definitions, and tool guidance providing context and guardrails around the user input. This suggests that most context engineering spend among Datadog customers is going toward optimizing repeating system prompts in heavily scaffolded agent systems.

TL;DR Katja Rantala thought her dream job involved international relations and diplomacy. Turns out leading people and companies through change is what she was meant to do. “Some days everything is on fire and it's a catastrophe. The important question is whether the big picture moved.” That is Katja Rantala's description of what it is like to work in an environment that keeps changing shape.

It's been one year since we issued an AI mandate inside Honeycomb, and we've been doing a lot of reflecting internally on how far we've come and how far we have yet to go.