Operations | Monitoring | ITSM | DevOps | Cloud

How to Improve Your Documentation with AI (CircleCI Chunk Tutorial)

AI coding assistants help you ship features fast, but documentation almost never keeps up. In this Ship Smarter session, we'll show you how CircleCI's Chunk autonomous CI/CD agent automatically analyzes your codebase, identifies documentation gaps, and opens a pull request with improvements. No manual writing required. In this video.

How to Ship AI-Generated Code to Production

AI writes code. But shipping to production? That still takes a software engineer. In this GitKon talk, Chris Kelly from Augment Code breaks down what it actually means to use AI-assisted development to write production-ready code, not vibe code. If you've been using AI coding assistants and wondering why the output doesn't always make it past code review, this is for you. Chris covers: Key takeaway: The engineers who will thrive aren't the ones who let AI do everything. They're the ones who know how to review, direct, and architect around what AI produces.

Observability and Security for the AI Era

Datadog has always been driven by a broader vision of helping teams understand and operate complex systems. In this session, you’ll hear from Michael Whetten, Product SVP, and Abrar Hussain, Senior Director, Product Management, as they share the latest updates across the Datadog product suite and discuss how that vision continues to shape the platform’s evolution and support the next generation of AI-driven applications.

Auvik Aurora and the Future of AI in IT Operations

We built something called Auvik Aurora, and before you scroll any further, I can already hear your thoughts. “Wait a second, Anto. Is this going to be another blog post giving me the hard sell on using AI?” Fair enough, I don’t think anyone would blame you, especially when we’re seeing AI adoption across nearly every industry, tool, hobby, workflow, or even . The blank is intentional, AI is everywhere, and chances are that you already know that it matters.

The Agentic Shift: Why the Unified Workspace is the Definitive Business Benchmark for 2026

The technology world moves in cycles of hype and utility. For the last three years, the narrative has been dominated by "Generative AI"-a phase defined by the novelty of chatting with bots or generating blocks of generic text. But as we navigate through 2026, that novelty has worn thin. Organizations have realized that having fifty different AI tools for fifty different functions isn't "innovation"; it is a logistical nightmare.

How AI Is Improving Marketing Cost Efficiency Through Smarter Resource Allocation

The entire marketing dynamic is no longer based on visibility, as it is now about precision. With growing market competition and customer journeys turning more complex, businesses fail to afford inefficient spending or delayed decision-making. At this point, AI plays a pivotal role not as a futuristic add-on but as the key logical engine.

How Custom AI Solutions Are Changing the Way Operations Teams Handle Scale

For businesses earlier, scaling operations has only focused on maximizing outputs. However, today the scenario is entirely different as it aims for enhanced efficiency, coordination, precision, and speed. This is extremely important across the increasing challenges in the entire business dynamics. Operation teams today often struggle with manual processes and an increasing workload. These are the main contributors to growing inefficiencies, performance lags, and decision-making.

How a Marketing Intern Ended Up Running Claude in a Terminal

Before I ever ran Claude in my terminal, I thought I already understood AI tools pretty well. Like most people, I had used ChatGPT, Google Gemini, and Perplexity for everyday tasks. Such as helping with schoolwork, organizing ideas, summarizing information, or getting through something faster when time was tight. They were useful, but they still felt separate from how real work happened.

Troubleshoot performance issues faster with the new Grafana Assistant integration for Database Observability

So your database is slow. Now what? Grafana Cloud Database Observability already gives you visibility into your SQL queries with RED metrics, individual execution samples, wait event breakdowns, table schemas, and visual explain plans. But visibility is just the starting point. You can see that a query's P99 latency spiked, but what should you do about it? You can see wait events like wait/synch/mutex/innodb firing, but what does that actually mean?