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How I Consolidated Duplicate Delivery Pipelines With Parameters and Build Tags

Two modules in a repository had near-identical Azure DevOps build and release definitions. A third would have required another pair. The delivery chain used five definitions: one change decider, two builds, and two releases. I consolidated it into one decider, one generic build, and one release with a deployment stage for each onboarded module. The count excludes package and pull request validation. Figure 1. The consolidation removed one duplicated build and one duplicated release. The Decider Passes the Module as a Run Parameter The old builds repeated the same image and chart tasks with different module values. The releases repeated the same deployment tasks with different variable groups. A shared build change required matching edits in both build definitions, and a shared deployment change required matching edits in both release definitions. Adding another module would have required another build and release pair. I named the shared definitions by product, service, scope, an...
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Splunk Preps Second Open Source LLM for Telemetry Data

Splunk is gearing up to make an artificial intelligence (AI) model for analyzing log data available on Hugging Face under an open source license. Additionally, Splunk at its .conf26 conference this week revealed it is developing a Universal Collector, expected to be available in beta in 2027, to streamline collection of all types of telemetry data using an instance of OpenTelemetry. Raja Mukhopadhyay, vice president of observability cloud for the Splunk arm of Cisco, said a large language model (LLM) that has been trained to reason across log data will make it possible for DevOps teams to observe AI applications and agents at scale. The Universal Collector, meanwhile, lays the foundation for unifying DevOps, IT operations and security operations by making it simpler to correlate events without having to deploy separate repositories for different types of telemetry data. Previously, Splunk has made available an open source Cisco Time Series Model 1.0 on Hugging Face that has been s...
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The Three Tiers of Agentic Incident Response: When to Trust AI Autonomy

AI agents are becoming operational teammates. They can ingest alerts, query logs and traces, correlate deployment events, consult runbooks and recommend or perform remediation. Their promise is not merely faster summarization: It is a shorter path from detection to safe recovery. But an agent that can explain an incident is not automatically an agent that should change production. Restarting a stateless pod, rolling back a customer-facing payment deployment and responding to a suspected credential compromise are materially different decisions. Each requires a different amount of evidence, human accountability and execution control. The practical answer is tiered autonomy. Tier 1 automates well-understood and reversible incidents. Tier 2 lets the agent investigate and recommend, while a human explicitly approves the action. Tier 3 keeps people in command for novel, complex or high-impact incidents while the agent accelerates evidence collection and hypothesis testing. The three tiers...
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The Most Dangerous Reliability Failures Aren’t Component Failures

When a production system fails, one of the first questions we usually ask is: What broke? Was it a storage service, a bad deployment, a memory leak, a dependency or a network problem? That is a useful place to start, but it can also narrow the investigation too early. Some production failures happen even when every component behaves as designed; the problem appears only in their interaction. Systems-safety methods give us a useful way to reason about this class of failure, and I ran into a good example in a large-scale provisioning system. When Everything Works and the System Still Fails The system created storage partitions on demand and, as part of that process, applied an access-control policy to every new partition. The policy logic lived in a shared library that already had another user: An operator CLI that engineers used to apply policies manually, one storage instance at a time. For the CLI, the design worked well. Each invocation read the current policy state of one storag...
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Factory Raises $200M as It Builds Agents Across the Software Lifecycle

Enterprise coding agent startup Factory announced it has raised $200 million at a $5 billion valuation, more than tripling its valuation from its last funding round five months ago. Founded in 2023, Factory develops AI agents for software engineering teams. Its platform is designed for work across the full software development lifecycle, including building, testing and maintaining applications. The company says its customers include Nvidia, Adobe, Palo Alto Networks, T-Mobile, and Blackstone. As AI coding startups proliferate, Factory is trying to differentiate itself by taking agents beyond discrete coding tasks and into coordinated software development workflows. In June, the company introduced Factory 2.0, which it describes as a “software factory” that brings tasks such as code review, security analysis, documentation, quality assurance and incident response onto a common agent platform. The platform also supports cloud, self-hosted and fully air-gapped deployments, which could ...
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LocalStack Acquires WonderTwin AI to Gain SaaS App Emulation Platform

LocalStack this week revealed it has acquired WonderTwin AI , a provider of an emulator of software-as-a-service (SaaS) applications that is used to build custom applications for those platforms. Colin Neagle, vice president of marketing for LocalStack, said the emulators WonderTwin AI has developed will be integrated into the company’s namesake emulation platform that application development teams currently rely on to emulate cloud services provided by Amazon Web Services (AWS). LocalStack and WonderTwin AI make it possible for application developers working on a local machine to build applications that are designed to be deployed on some type of external cloud platform using a local sandbox to test and validate integrations without having to connect to a service or build against a live application programming interface (API). That issue has been especially critical in an era where more code will soon be generated by AI coding agents that may for one reason or another circumvent the...
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Java 27 Tackles Post-Quantum Security and a Faster Patch Cadence

Security teams have spent the past few months watching frontier AI models surface vulnerabilities faster than most organizations can patch them. Oracle’s answer, at least for the world’s most widely deployed programming language, is to change how often it ships fixes — and to get ahead of a threat most enterprises haven’t started planning for yet. Java 27, released September 15, moves Oracle toward monthly critical security patch updates alongside its existing quarterly cycle. It also delivers the platform’s biggest cryptography milestone in years: post-quantum hybrid key exchange for TLS 1.3, built to protect data now from being decrypted later once quantum computers catch up. “Java 27 continues that proud tradition by providing a stable foundation for today’s enterprise workloads and innovative AI and post-quantum cryptography capabilities,” said Georges Saab, senior vice president of Oracle Java Platform and chair of the OpenJDK governing b...
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