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Production Validation: The Missing Layer in Enterprise Releases
Kawaljeet Singh Chadha | | auditability, business validation, business-critical systems, change management, data quality, deployment controls, DevOps governance, enterprise devops, Enterprise software, exception-handling, operational approval, production assurance, production readiness, production validation, reconciliation, regulated environments, release governance, release management, release readiness, software testingProduction validation adds a critical business-control layer between testing and deployment, combining data checks, exception review, approvals, reconciliation and operational readiness before a release reaches production ...
Tricentis Preps Wave of Additional AI Testing Capabilities
Tricentis Labs is providing early access to multiple artificial intelligence (AI) capabilities that it is gearing up to roll out later this year via a Tricentis Transform initiative, including an autonomous AI ...
The Missing Runtime for Long-Running AI Agents
Anuj Kapoor | | agent governance, agent orchestration, agent runtime, AI agents, AI observability, AI resiliency, Azure AI Foundry, Azure Durable Functions, devops, distributed systems, durable AI agents, durable workflows, Enterprise AI, human-in-the-loop, long-running AI agents, multi-agent systems, platform engineering, stateful AI, workflow orchestrationEnterprise AI agents need more than stronger models. They need durable execution environments that can coordinate multi-step workflows, survive failures, pause for human review and resume reliably after disconnects or delays. AI ...
Automated Diagnosis Isn’t Automated Understanding: What Postmortems Teach Us About Building Trustworthy Incident AI
Jyostna Seelam | | AI incident response, AI observability, AI reliability, AIOps, alert correlation, automated incident response, causal reasoning, dependency graphs, devops, Engineering leadership, human-in-the-loop, incident diagnosis, incident management, LLM operations, non-deterministic systems, on-call engineering, postmortems, RAG systems, root cause analysis, SREAI incident tools can reduce alert noise, but real root-cause diagnosis requires causal reasoning, live dependency context, uncertainty handling and strong postmortem data ...
AI Can Generate Your Infrastructure. Can Your CI/CD Pipeline Trust It?
Oreoluwa Omoike | | AI code vulnerabilities, AI coding assistants, AI-generated code, AI-generated infrastructure, Checkov, CI/CD security, Conftest, devsecops, IaC security, infrastructure as code, infrastructure security, Kubernetes security, OPA, platform engineering, policy as code, provenance tracking, secret scanning, secure CI/CD, Terraform security, tfsec, Trivy, TruffleHogAI-generated infrastructure code is exposing a growing security gap, pushing platform teams to add stronger automated gates, provenance tracking and human review before Terraform, Kubernetes and CI/CD changes reach production ...
Your AI Coding Budget Is Becoming a Variable Cloud Bill
Carla Castillo | | agentic coding, AI coding assistants, AI coding costs, AI cost management, AI credits, AI developer tools, AI Governance, cloud economics, cost per developer, developer productivity, devops, engineering spend, FinOps for AI, GitHub Copilot, model mix, platform engineering, premium models, SaaS budgeting, token billing, usage based billingAI coding assistants are becoming a variable, usage-based engineering cost, forcing platform and DevOps teams to apply FinOps practices to models, credits, utilization and multi-vendor spend ...
Is Java Enterprise Ready for AI? Absolutely
AI is transforming software engineering. For enterprise Java developers, the key question is whether Java and Jakarta EE are prepared to integrate AI into enterprise applications. The answer is yes. Java and ...
CI/CD for AI-Enabled Applications: Why Traditional Deployment Pipelines Need to Evolve
Prem Kumar Gadhanki | | artificial intelligence, CI/CD, continuous delivery, devops, MLOps, software deliveryTraditional CI/CD pipelines are optimized around a familiar assumption: source code changes, automated tests validate the change, a build artifact is produced, and the application is promoted through environments. AI-enabled applications complicate ...
The Blind Spot Between Back‐End Metrics and User Experience
Engineering teams often face a frustrating scenario when application performance monitoring (APM) shows perfect Apdex scores and negligible error rates, yet user retention drops due to poor front‐end performance. The solution requires ...
Why DNS, DHCP, and IPAM Can No Longer Live in Separate Silos
Ask a network engineer where their DNS records live, and the answer is rarely straightforward. Some zones reside on Windows DNS servers, others are hosted in cloud-managed services, while DHCP scopes are ...
The AI Agent Race Is On. But Are We Watching the Right Race?
Claude Code, OpenAI Codex, GitHub Copilot, Cursor and a growing field of challengers are competing to define the future of software development. A new Techstrong special report examines who is ahead, how ...
Report Shines Spotlight on 91 Vulnerabilities Fixed in Latest Update to Spring Framework
Mike Vizard | | AI cybersecurity, AI vulnerabilities, application security, Broadcom, CVEs, devsecops, Java security, open source security, open source vulnerabilities, patch management, security automation, software components, Software Supply Chain, sonatype, Spring framework, Spring vulnerabilities, technical debt, virtual patching, vulnerability managementSonatype says 91 Spring vulnerabilities affecting more than 209,000 software components highlight how AI is accelerating vulnerability discovery and creating a new patching challenge for DevSecOps teams ...
