AI SRE Agent, End to End: AURA Takes an Incident from Alert to Fix
Checkout starts failing because the cart service keeps dying. The real cause is a recent pull request that let memory grow with every add to cart.
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You don't know what your model is going to do when you tell it what to do.
Give a model permission to act on your computer and you're trusting it will behave the way you expect. It might not. In this clip from our Braintrust conversation, Adam Berman, engineering leader at Semgrep, breaks down why a backdoored model is a different threat model than backdoored software. You can't fuzz-test your way to finding it, and you often can't detect it until it's already acting the way it shouldn't.
Minga's developers point AI tools at their own infrastructure
Matt Zytaruk, VP of Technology at Minga, on what changed once his developers could point their AI tools at their own infrastructure. Minga checks in 1.5 million students across the eastern seaboard every school morning. They run that platform on Control Plane, which gives their team an MCP server, a CLI, and Terraform-native resources — so a developer can investigate an issue and generate the code for the change without waiting on anyone.
AI productivity is showing up in the data
Average throughput across all projects on CircleCI increased 59% YoY.
Alert fatigue, AI triage, and incidents: Lessons from observability experts at Cyera, PlayHQ & NAB
Observability looks perfect in a slide deck – in practice, it's messier. In this panel, engineering leaders from Cyara, PlayHQ, and National Australia Bank share what really happened when they scaled observability: unexpected cloud bills, alert fatigue, a weekend database outage caught by an AI-assisted triage agent, and a vendor dispute settled by a single chart. They also cover moving beyond legacy tooling, using AI to close the PromQL skills gap, and what's next – from agentic SDLC integration to continuous profiling. Real stories, real numbers, real lessons.
Claude Code sped up the wrong part of your job
#claudecode #optimization #aicoding #observability
AI speeds up delivery. Here's how IT leaders manage the risk when AI-generated code hits production.
AI can accelerate speed to market, but for IT leaders it also raises a harder question: can you prove how an AI-generated change reached production? Chris Yates (SVP, Managing Director of Data & Architecture, Republic Bank) explains how his team builds a full evidence trail for every change, using version-controlled deployment tooling like Redgate Flyway Enterprise, so governance becomes a guardrail rather than a brake on speed.
Why AI Adoption Fails Without Operational Maturity First
Most MSPs are already experimenting with AI in some form, and every vendor at every conference has an AI for MSPs pitch ready. Few have stopped to check whether their own operations are solid enough to scale. Our 2026 IT Trends Report found that three-quarters of IT leaders believe they have an AI policy, while fewer than half of help desk staff agree. That’s the real risk: AI doesn’t fix drift, unclear ownership, or gaps between what’s documented and what’s actually happening.
Find Resilience Risks Automatically, Then Confirm Them | Harness Blog
Resilience Testing (RT) Agents scan your CD pipelines and Kubernetes workloads for resilience risk before it reaches production, then generate and run the chaos experiments or load tests needed to confirm it.
Build your own Bits Agent with Datadog Bits Agent Builder
Datadog Bits Agent Builder lets you build AI agents that use your observability data to automate operational tasks. In this walkthrough, see how to build an agent that analyzes monitor and alert activity, identifies patterns, and provides actionable recommendations to improve your monitoring strategy. With Bits Agent Builder, you can give agents access to Datadog data and tools, customize their instructions and models, and run them automatically to continuously analyze and act on your environment.