Tag: AI Governance
Google Adds Hooks to Gemini CLI for Customized AI Workflows
Tom Smith | | AI coding assistant, AI Governance, AI hooks, AI lifecycle control., AI policy enforcement, AI workflow optimization, ci/cd tools, customize AI assistant, Gemini CLI hooks, middleware AI tools, software development, software lifecycle engineering, user privilege managementEnhance Gemini CLI with new hooks to customize AI assistant workflows without code changes. Improve control and optimize AI for development teams ...
Dynatrace Delivers on Promise to Observe AI Coding Tools from Google
Mike Vizard | | Agent-to-Agent A2A protocol, agentic AI, AI agents, AI Governance, AI telemetry, AI workflow observability, AIOps, DevOps observability, enterprise AI monitoring, Gemini CLI Extension, Gemini Enterprise, Linux Foundation, Model Context Protocol MCP, observability platform, root cause analysis, software development lifecycle SDLCDynatrace announced new integrations with Google Cloud Gemini Enterprise and Gemini CLI, using agentic AI, A2A protocol, and MCP servers to enhance observability, root-cause analysis, and DevOps governance across AI-driven workflows ...
Why Your AI Agent Strategy is Failing (and How to Fix It): The Microservices Playbook for AI Agents
Lokhesh Ujhoodha | | agentic AI, AI agent transparency, AI agent trust, AI agents, AI architecture, AI Governance, AI infrastructure, AI microservices, AI observability, AI ROI, autonomous agents, CIO AI strategy, Enterprise AI, enterprise AI strategy, LLM adoption, microservices architecture, monolithic AI agentsDespite billions in AI investment and countless vendor promises, most enterprises are still treating AI agents like glorified copilots rather than autonomous systems. After working with numerous enterprise customers implementing AI agents across various ...
The Future of DevOps Still Has a Pulse
Anshul Sharma | | agentic DevOps, AI (Artificial Intelligence), AI Governance, AI infrastructure, devsecops, kubernetesOver the last few years, we have watched our industry get swept up in the promise of AI agents. The pitch is compelling: tell a system “Deploy this workflow and roll back ...
How to Escape the Talent Valley
Dustin Lehr | | ai, AI Governance, AI in software development, application security, developer careers, devsecopsAcross the tech industry a disconcerting trend is emerging, job losses at the hand of a seemingly more efficient and cost-effective employee, artificial intelligence (AI). Software developers in particular have felt the ...
The MLSecOps Era: Why DevOps Teams Must Care about Prompt Security
Alex Vakulov | | AI attack surface, AI chaos testing, AI Governance, AI supply chain risk, autonomous agent security., CI/CD pipeline security, CodeShield, DevOps AI, ISO 42001, LlamaFirewall, LLM security, MLSecOps, model drift, NIST AI RMF, prompt injection, prompt validation, PromptGuard, PromptOps, RBAC for AI, red team AI, runtime monitoring, secure AI pipelinesAI-driven software delivery introduces new risks, especially prompt manipulation within CI/CD workflows. This article details the emerging fields of PromptOps and MLSecOps and offers practical strategies for securing prompts, models, and pipelines ...
JFrog Adds Ability to Track Usage of AI Coding Tools
Mike Vizard | | AI API management, AI code auditing, AI coding tools, AI compliance tracking, AI Governance, AI in DevOps, AI model inventory, AI risk mitigation, AI-generated code, application security, code risk detection, devsecops, Large Language Models, LLM security, MLOps, SCA, secure coding practices, Shadow AI detection, software composition analysis, software provenance, Software Supply Chain Security, software vulnerabilities, source code scanningJFrog introduces AI-Generated Code Detection and Shadow AI Detection tools to identify AI-created code, track model usage, and enhance DevSecOps governance across software supply chains ...
From Code to Confidence: Building AI Apps That Earn User Trust
Chris Sheehan | | AI bias, AI ethics, AI Governance, AI quality assurance, AI testing, AI trust, Applause, bias detection, explainable AI, fairness, feedback loops, human-centered AI, inclusive testing, MLOps, model transparency, responsible AI, trustworthy AI, user confidence, user experienceAs 65% of users report issues with AI applications, trust has become the new UX battleground. Learn how developers can build fair, transparent, and reliable AI systems through human-centered testing, inclusive feedback ...
Tabnine Adds Agents Capable of Automating Workflows to AI Coding Platform
Mike Vizard | | agentic AI, AI agents, AI compliance, AI for DevOps, AI Governance, AI tools, autonomous coding, Code Generation, code intelligence, Context Engine, debugging, devops automation, Enterprise AI, flat-rate pricing, LLM integration, Refactoring, software development lifecycle, technical debt, workflow automationTabnine introduces Tabnine Agentic, a new generation of AI agents that automate multi-step DevOps workflows including refactoring, debugging, and documentation. Built on Tabnine’s Context Engine, these agents bring governance, cost control, and ...
Rewriting the Rules of Software Quality: Why Agentic QA is the Future CIOs Must Champion
Chirag Gandhi | | adaptive testing, agentic QA, agentic systems in DevOps, AI DevSecOps, AI for CIOs, AI Governance, AI in enterprise software, AI in software testing, AI quality automation, AI software lifecycle, AI software quality, AI-driven QA, AI-enabled QA frameworks, autonomous QA systems, autonomous quality assurance, continuous testing with AI, Enterprise AI, ethical AI testing, generative AI QA, intelligent agents, QA automationDiscover how Agentic QA is transforming enterprise software testing with autonomous AI systems that embed quality into every stage of development ...
Before You Go Agentic: Top Guardrails to Safely Deploy AI Agents in Observability
Pankaj Thakkar | | agentic AI, AI audit trails, AI cost management, AI deployment controls, AI Governance, AI guardrails, AI in DevOps, AI observability safety, AI operations, AI policy validation, AI rate limiting, autonomous agents, data privacy, human-in-the-loop, KubeCon 2025, observability, OpenTelemetry, role-based access control, self-healing infrastructure, zero-trustObservability platforms are evolving from passive monitors to active participants. Agentic AI promises a self-healing infrastructure that detects anomalies and fixes issues before users notice, reducing resolution time from hours to minutes ...
The Developer’s Guide to Agentic AI: The Five Stages of Agent Lifecycle Management
Dan Fernandez | | adaptive AI systems, agent lifecycle management, agent-based systems, agentic enterprise, AI adaptability, AI agents, AI ALM, AI development lifecycle, AI Governance, AI in enterprise, AI innovation, autonomous agents, business automation, continuous AI improvement, enterprise transformation, generative AI, intent-driven agents, Salesforce Agentforce, self-improving AI, Stanford SIRIUS frameworkDiscover how AI agents evolve from task executors to adaptive, self-improving systems through Agentic Lifecycle Management, driving agility and innovation ...
