Latest Articles
How to Build a Durable Change-Control Gate for AI Agents
AI agents that trigger real-world changes need more than confidence scores. A durable change-control gate should recheck policy, require approval for consequential actions, enforce idempotency and verify the result before retrying ...
Production Validation: The Missing Layer in Enterprise Releases
Production 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 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 agent, ...
The Missing Runtime for Long-Running AI Agents
Enterprise 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
AI 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?
AI-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
AI 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
Traditional 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 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
Sonatype 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 ...
Why “Tokenmaxxing” Was Always the Wrong Way for Developers to Measure AI Productivity
The term "tokenmaxxing" left the developer lexicon just as quickly as it arrived, and like most viral technology concepts, it means different things depending on who's using it. In practice, the term ...

