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Why agentic AI is the future of IT change management

Every enterprise depends on continuous changes to its IT environment. New code releases, infrastructure updates, configuration changes, and security patches are all crucial to support continuous innovation. These same changes are also a leading source of operational risk and one of the most common causes of failures at the network, infrastructure, and software layers, resulting in outages.

Building reliable dashboard agents with Datadog LLM Observability

This article is part of our series on how Datadog’s engineering teams use LLM Observability to iterate, evaluate, and ship AI-powered agents. In this first story, the Graphing AI team shares how they instrumented their widget- and dashboard-generation agents with LLM Observability to detect regressions and debug failures faster. Visibility into how large language model (LLM) applications behave in real time is essential for building reliable AI-driven systems at Datadog.

How to Build Media Operations That Survive Full AI Automation

By the end of 2026, you will upload a product image and a budget to Meta, and its AI will generate the creatives, pick the audience, allocate spend across surfaces, and optimize in real time. Google’s Performance Max already automates bidding, asset selection, and cross‑channel allocation across Search, Shopping, YouTube, Display, and more.

AI in Contact Centers: Capabilities, Limits, and the Missing Decision Layer

AI in contact centers refers to the use of artificial intelligence technologies to automate customer interactions, support agents in real time, analyze conversations, and improve operational efficiency. In practice, this includes chatbots, virtual agents, intelligent routing, agent assist tools, sentiment analysis, and automated quality assurance systems designed to increase speed, consistency, and scale.

How AI OCR Is Reshaping Automated Data Extraction in Large-Scale Business Operations

Businesses handle massive amounts of data every day. Such data is obtained from invoices, bills, contracts, applications, and many other documents. Most of these documents are distributed in the form of scanned copies and images. As a result, whenever organizations resort to manual data entry in processing such data, the process turns out to be slow and filled with errors. However, to avoid these issues, organizations are now turning to AI-OCR solutions for better data extraction and increased operational efficiency.

How the Right Business Essentials Support Long-Term Efficiency

Running a business smoothly depends on many small details. One of the most important things is having the right supplies to do daily work. If people don't have what they need, tasks slow down, and problems pile up. And efficiency - the ability to get things done well and on time - suffers. Well, it's worth noting that workplace essentials aren't glamorous. They're not flashy. But they are the foundation of daily operations. When these basics are reliable, teams can focus on real work instead of scrambling for tools or replacing worn-out items.

The CES Hangover: 3 Expensive Hardware Fails That Were Actually Software Problems

The dust has settled on Las Vegas. We saw transparent TVs, cars that drive sideways, and enough “AI-powered” toothbrushes to confuse a dentist. CES is incredible at selling the dream of hardware. The demos are slick, the lighting is perfect, and everything works on the showroom floor. But as engineers, we know the dirty secret of CES: The hardware is the easy part.

Agentless First, Agents When Needed: A Hybrid Approach to Security Telemetry

Security data collection has become a first-class architectural concern for modern SOCs. Once collection is treated as a dedicated layer, separate from analytics and detection, the next question becomes practical: how should telemetry be collected in a way that aligns with this architecture? In the previous article, we examined why this shift occurred. Here, we focus on how different collection models (agent-based, agentless, and hybrid) fit into modern security data collection architectures.