Harness today added two tools to track and analyze the impact code generated by artificial intelligence (AI) coding tools is having on application development. An AI Development Lifecycle (DLC) tool installs agent software on a developer’s machine to track adoption, sessions, and the code created across every coding agent, while a Cloud & AI Cost […]
IBM, Red Hat Launch Project Lightwell to Secure Open Source Software from Frontier Models
IBM and Red Hat are bringing together what they’ve learned from frontier AI models and 20,000 engineers to launch Project Lightwell, a $5 billion initiative aimed at helping enterprises better secure their open source software, work that has become more challenging in the age of such models as Anthropic’s Claude Mythos Preview. Mythos and similarly […]
More Signal, Less Clarity: The Observability Paradox No One Wants to Talk About
Record observability spending is driving up MTTR. Discover why tool sprawl and excessive dashboard data cause cognitive overload for on-call engineers, and how to fix it.
Why Agent Skills Are the Next Evolution of Software Development
The emergence of agent skills — modular, reusable blocks of natural language instructions and metadata — is transforming the developer’s role.
The Future of Salesforce DevOps: Preparing for the AI Era
By establishing a robust DevOps foundation now, organizations can leverage these emerging predictive capabilities to transform reactive pipelines into proactive, self-correcting release architectures.
The End of Alert Fatigue: How AI-Powered Observability is Transforming SRE Teams in 2026
Alert fatigue among Site Reliability Engineering (SRE) teams has reached a breaking point, with responders drowning in thousands of weekly notifications where only 3% genuinely warrant attention. This massive volume of noise—driven by fragmented monitoring tools and rigid, threshold-based alerting—stifles innovation, spikes on-call burnout, and compromises system reliability. Fortunately, AI-powered observability and AIOps platforms are transforming incident management. By unifying telemetry across metrics, logs, and traces, intelligent systems can correlate signals, execute automated root cause analysis, and trigger self-healing remediation. This shift reduces alert volumes by up to 95% and slashes mean time to resolution (MTTR) by 40–58%, allowing engineers to pivot from reactive firefighting to proactive reliability engineering.
5 Ways Agentic AI is Redefining DevOps Architecture for Self-Healing CI/CD Systems
The era of the flaky test as a simple annoyance is over. As enterprises shift from deterministic applications to agentic AI, flakiness has evolved into a structural bottleneck for traditional CI/CD pipelines reliant on rigid, binary assertions. Because AI agents produce “Y-like” rather than exact results, DevOps architecture must fundamentally change. This article explores the transition from simple pipeline automation to true autonomy—detailing how multi-agent networks utilize predictive failure detection, self-healing test repair, autonomous incident remediation, and adaptive security scanning to create pipelines that actively problem-solve and adapt to code changes in real time.
JFrog Report Surfaces Need for Rapid DevSecOps Change in AI Era
A report published by JFrog finds that cybercriminals are now increasingly targeting the artificial intelligence (AI) tools and platforms used by application development teams. Based on an analysis of 18.2 billion artifacts managed via the JFrog Platform, security researchers discovered 969 AI agent skills carrying high-impact payloads in addition to 495 malicious AI models on […]
On-Call: The Silent Force Shaping Engineering Culture
There is a silent force shaping engineering culture inside every technology organization. It affects productivity, team morale, psychological safety, and long-term retention. And yet, it is rarely discussed in executive meetings or reflected in meaningful KPIs. That force is on-call. On-call is one of the most direct touchpoints engineers have with the reality of the […]
Why DORA Metrics Look Different When AI Is Part of Your Development Workflow
DORA metrics have been a reliable compass for engineering teams for over a decade. Deployment frequency, lead time for changes, change failure rate, mean time to recovery, and reliability give teams a shared language for talking about delivery performance. The research behind them is solid, the benchmarks are well-established, and most engineering leaders know what […]
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