Business of DevOps
Debian’s AI Vote Bets on Accountability Instead of a Ban
Tom Smith | | AI coding, AI Governance, AI policy, AI-assisted development, AI-generated code, code review, confidential data, Debian, developer productivity, devops, generative AI, Human Oversight, open source, software development, software securityDebian’s new AI contribution policy avoids bans and mandatory disclosure, instead making developers responsible for reviewing AI-generated work and protecting sensitive data ...
OpenAI Cuts Off Cursor’s Model Access After SpaceX Acquisition
OpenAI is ending Cursor’s direct access to its AI models after Elon Musk’s SpaceX acquired its parent company, Anysphere. This is the next move in the increasingly bitter conflict between Elon Musk ...
Broadcom Launches Trusted Artifact Service for Spring Framework
Mike Vizard | | AI coding, Apache Tomcat, application security, Bitnami Secure Images, Broadcom, container security, dependency security, devsecops, java, kubernetes, node.js, open source security, open source vulnerabilities, patch management, Python, SBoM, secure software development, software artifacts, Software Supply Chain Security, Spring framework, Spring Security, Tanzu, TrueSource Trusted Artifacts, VMware explore, vulnerability managementBroadcom launches TrueSource Trusted Artifacts to provide hardened Spring dependencies, secure open source packages and automated vulnerability remediation ...
A Simple Website Summary Just Exposed the Limits of AI Coding Guardrails
Tom Smith | | agentic AI security, AI agent security, AI coding assistants, AI guardrails, AI security, Anthropic, Auto Mode, Claude Code, Claude Opus 5, devops security, indirect prompt injection, Johann Rehberger, network egress, platform engineering, prompt injection, Python module shadowing, RCE, remote code execution, sandboxing, struct.pyClaude Code’s Auto Mode was bypassed in an attack chain that turned a routine webpage summary into remote code execution, highlighting the limits of AI agent guardrails ...
How to Build a Durable Change-Control Gate for AI Agents
Daniel Martin | | agentic AI, AI agents, AI automation, AI Governance, AI safety, approval workflows, change control, devops, durable workflows, human-in-the-loop, idempotency, LangGraph, NIST AI RMF, policy enforcement, production deployment, release controls, tool-using agents, workflow governanceAI 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
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 ...
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 ...
Beyond Log Search: What We Learned Building a RAG-Based Incident Diagnosis System
Abhimanyu Garg | | AI for SRE, AI hallucinations, AI incident diagnosis, AIOps, devops, devops automation, FAISS, incident management, incident remediation, incident response, knowledge management, knowledge retrieval, LLM grounding, LLM incident response, observability, postmortems, production AI, RAG AIOps, re-ranking, retrieval-augmented generation, root cause analysis, Runbooks, semantic search, SREA RAG-based AIOps framework can cut incident diagnosis time by grounding LLM reasoning in real runbooks, tickets and postmortems, improving root-cause accuracy while giving SREs source-backed answers they can trust ...
Elevating Technical Documentation: Using Predictive AI to Standardize Visual Knowledge Bases
Nazim Ghori | | AI documentation, AI visual validation, architecture diagrams, CI/CD documentation, computer vision, developer documentation, developer experience, devops automation, DevOps documentation, documentation automation, documentation drift, documentation quality, documentation quality gates, Git workflows, knowledge bases, OCR, predictive AI, screenshot validation, technical documentation, visual validationEvery release changes documentation alongside the code. Developers depend on that documentation during implementation, maintenance and troubleshooting. A diagram becomes misleading once services are renamed or integrations change without a corresponding documentation ...
