Tag: AI Governance
Still Using API Keys for Your AI Agent? Here’s When it’s Time to Upgrade
Rishi Bhargava | | agent security, agentic AI, AI agent, AI Governance, AI security, API Keys, identity and access management, OauthAPI keys got you here. They won’t get you where you’re going. OAuth isn’t a future upgrade. It’s the foundation your agents should have been built on from the start. ...
Agentic DevSecOps: AI Security Co-Pilots for Your CI/CD Pipeline
Joydip Kanjilal | | agentic AI, AI copilots, AI Governance, AI incident response, AI pipeline management, AI security, AI security agents, AI threat detection, AI vulnerability scanning, AI-driven security, AI-powered DevSecOps, application security, autonomous AI agents, autonomous security testing, CI/CD security, Cloud Security, continuous security monitoring, cybersecurity automation, devops security, devsecops, devsecops automation, infrastructure security, intelligent automation, real-time threat intelligence, SDLC security, Secure software delivery, security automation, security operations, security orchestration, security posture management, software development lifecycle, software security, threat remediationThe emergence of AI has brought endless possibilities and innovative opportunities in today’s ever-changing, fast-paced technology landscape. AI is helping development teams produce software significantly faster than ever before. AI-enabled DevSecOps tools ...
The Death of the Four Golden Signals: Designing Telemetry for Non-Deterministic Infrastructure
Jyostna Seelam | | AI architecture, AI compliance, AI Governance, AI guardrails, AI infrastructure, AI monitoring, AI observability, AI operations, AI operations strategy., AI performance metrics, AI platform monitoring, AI reliability, AI resilience, AI safety, AI SLIs, AI SLOs, AI system reliability, AI telemetry, AI trust, Enterprise AI, generative AI monitoring, guardrail intervention rate, hallucination detection, LLM monitoring, LLM observability, modern SRE, non-deterministic systems, Observability Engineering, OpenTelemetry, platform engineering, production AI, RAG observability, RAG systems, semantic drift, site reliability engineering, SRE, time to first token, token throughput, TTFT, vector database monitoringIn complex software systems, our traditional definition of operational health has always been comfortably binary. For over a decade, site reliability engineering (SRE) teams have relied on the industry-standard ‘Four Golden Signals’ ...
When Should a DevOps Agent Act Without Human Approval?
Bala Priya C | | agentic DevOps, AI agents, AI Governance, approval fatigue., automated remediation, blast radius, DevOps autonomy, human-in-the-loopDeploying AI agents in DevOps requires a granular approach to autonomy based on reversibility, blast radius, signal quality, and time sensitivity to build organizational trust ...
The Next AI Breakthrough Isn’t Generative, It’s Agentic
Lee McClendon | | agentic AI, Agentic Testing, AI Governance, AI Orchestration, AI Productivity ROI, AI quality assurance, Autonomous Systems, business intelligence, Enterprise AI Deployment, human-in-the-loop, risk mitigation, software development lifecycle SDLC, Trustworthy Speed., Vibe Coding RiskGenerative AI first captured the world’s attention by producing content at an unprecedented speed. From lines of code and documentation to frameworks and designs, content creation is now readily available at the ...
Agentic Systems are Breaking Reliability Frameworks
Saqib Jan | | Agentic Systems, AI Governance, Behavioral Boundaries, Capability Envelope, Circuit Breakers for AI, Deterministic Testing, Distribution-based Validation, Monte Carlo Testing, Oracle Cloud Infrastructure., Probabilistic Failure, prompt injection, Runtime Assertions, Shahid Ali Khan, SRE for AI, TestMu AIAgentic AI systems introduce "silent failures" that bypass traditional SOC alerts. Learn why DevOps and Security teams must shift from deterministic assertions to distribution-based testing and runtime behavioral boundaries ...
Sorry, Charlie, StarKist Wants AI With Good Taste
Alan Shimel | | Accelerate, accountability, AI alignment, AI Governance, behavioral generalization, blameless postmortems, character in AI, constitutional alignment, Culture Change, devops, emergent misalignment, ethical AI, guardrails, incentive design, incentives, insecure code, interdisciplinary alignment, model disposition, model governance, moral behavior, safety engineering, site reliability engineering, training data culture, training pipelines, trustworthiness, virtuesA surprising AI experiment showed that feeding a model sloppy code didn’t just produce bad programming, it produced bad behavior. The result points to something philosophers and DevOps engineers have long understood: ...
Why AI Makes Requirements a Runtime Artifact
Tom Hollowell | | AI behavior, AI Governance, AI requirements, continuous verification, MLOps, model drift, production monitoring, requirements traceability, runtime constraintsIn traditional software, requirements are static design-time artifacts. In AI-enabled systems, they must be continuously observed and enforced in production. Learn how AI collapses the boundary between design-time and runtime, shifting requirements ...
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 ...
