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datadog

How we built data-driven AI Golden Paths at Datadog

Sep 9, 2026 By Datadog In Datadog
As teams rush to adopt AI, they often find themselves with conflicting workflows unique to each individual developer. To manage costs and promote good development practices, organizations need to establish Golden Paths around AI usage. AI Golden Paths are standardized flows that help developers work with agents more reliably and effectively. But how do you sift through all the possible workflows to decide what these Golden Paths should be?
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honeycomb

AI Norms & Values, Part 3 of 3: Things We Hold True

Sep 9, 2026 By Charity Majors In Honeycomb
Welcome to the third and final part of our series on AI norms and values. Parts of this doc were extracted and published separately on substack; as a whole, they describe the principles we hold pertaining to technology and AI, and the ethical commitments we make to each other and our customers. We set out to write about AI, and ended up writing about ourselves. These documents are not meant to be aspirational ones; they are derived from how we do our work every day in honeycomb.
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selector

On a Network, an Agent Acts Where the Blast Radius Is Largest

Sep 9, 2026 By Dallon Robinette In Selector
Every network engineer carries an instinct that outsiders mistake for caution: a change in one place can travel. Reroute a path, push a policy, drop an interface, and the effect can ripple across campus, data center, WAN, and cloud before the first alert is read. The blast radius of a network change is the reason operators move deliberately, and it is the single most important thing an AI agent takes on the moment it is allowed to act on the network instead of merely describe it.
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harness

AI Code Review Loop in the Terminal: Introducing Harness CLI for Harness Code

Sep 9, 2026 By Mohit Suman In Harness
Every developer knows the fatigue of the "12-tab code review dance": Agents have become first class citizens in SDLC and AI coding agents author code alongside human engineers, thus the above context switching destroys flow state. GitHub's gh CLI proved developers love the terminal, but modern delivery is tied to AI reviews, pipeline executions, risk scoring, and autonomous agents, not just git hosting.
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gitkraken

Questions to Ask About AI Agent Orchestration

Sep 9, 2026 By Jade Nangah In GitKraken
Running AI coding agents in parallel across repositories is no longer experimental. It’s how high-performing engineering teams ship faster. But the tools you pick to orchestrate those agents can either multiply your output or introduce new bottlenecks. GitKraken gives your team a purpose-built surface for AI coding agent orchestration through Kepler, its agent-agnostic development environment. Before you commit to any orchestration tool, though, you need to ask the right questions.
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Top Legal AI Tools for Reducing Manual Work Across the Personal Injury Case Lifecycle in 2026

Top Legal AI Tools for Reducing Manual Work Across the Personal Injury Case Lifecycle in 2026

Sep 9, 2026 By Mika Kankaras In OpsMatters
Personal injury cases create a lot of work that has little to do with making legal decisions. Someone still has to review medical records, find details buried in case files, build chronologies, prepare demands, draft documents, organize evidence, and keep case information up to date. Legal AI can take some of that work off the team's plate. The most useful tools are not necessarily the ones with the most features. They are the ones that address the parts of a case where attorneys, paralegals, and case managers are spending hours on repetitive work.
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Assisted, Augmented or Agentic? Choose Your Splunk Starting Point

Sep 8, 2026 By Splunk In Splunk
Episode two of Beyond the Thread explores how organizations can leverage a solid data foundation for AI-driven actions. Hosted by Courtney Wright and featuring experts Greg Ainsley-Malik and Sonal Pardeshi, the discussion delves into the Cisco Data Fabric, powered by the Splunk platform, and its role in transforming machine data into actionable insights. The episode highlights the journey towards agentic operations, addressing the challenges faced in moving from AI-ready data to effective implementations, and examines different adoption strategies that organizations may pursue.
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netdata

Introducing Infrastructure Knowledge: Teach Netdata AI What Your Metrics Can't Show

Sep 8, 2026 By Shyam Sreevalsan In netdata
Netdata AI sees everything your infrastructure does: every metric, every anomaly, every alert. It does not see what your infrastructure is: which services matter, which host is supposed to run hot, who owns what, what your team considers normal. Without that context, “CPU at 91%” is just a finding. With it, it might be a machine doing exactly its job.
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honeycomb

Wide Events vs. Three Pillars: AI Observability Costs

Sep 8, 2026 By Nick Travaglini In Honeycomb
As agentic AI workflows gain traction within organizations, those organizations are asking how to account for their behavior while keeping costs manageable. Some are sticking with the old three pillars of observability approach: take a measurement to create a metric, record output to a log, and track serial progress with a trace. Each of these is useful, but treating them as distinct formats from the start means paying for them distinctly too. Separate storage doesn't come cheap.
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