Contributed Content
Certificate Renewal Is a Deployment Workflow, Not a Cron Job
Franz Alliod | | automation, certificate management, Certificate Renewal, devops, kubernetes, securityCertificate renewal is often treated as a scheduled task: run an ACME client, obtain a new certificate, and move on. In practice, that view is too narrow for production systems. A certificate ...
When AI Coding Agents Become Malware Delivery Systems
Samuel Ogbonna | | AI coding agents, AI security, devsecops, github, prompt injection, Software Supply ChainAI coding agents are becoming part of everyday development work. Developers use them to find libraries, configure projects, troubleshoot installation problems, and set up new tools. An agent can search GitHub, read ...
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 ...
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 ...
Why “Tokenmaxxing” Was Always the Wrong Way for Developers to Measure AI Productivity
Drew Thompson | | ai, AI Development, AI security, application security, developer productivity, software developmentThe 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 ...
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 ...
Scalable Jenkins Management: Empowering Enterprises With Centralized Control for 150+ Instances
Thrivikram Eskala | | ansible, ArgoCD, AWS, azure, CI/CD Automation, developer platform, devops automation, ELK stack, gitops, grafana, Helm, Jenkins, Jenkins administration, Jenkins backups, Jenkins control plane, Jenkins fleet, Jenkins management, Jenkins observability, Jenkins rollback, Jenkins upgrades, kubernetes, multi-cloud CI/CD, platform engineering, Prometheus, self-service platformA centralized Jenkins control plane can reduce fleet sprawl, automate upgrades and backups, improve observability, and give application teams safe self-service across large multi-cloud CI/CD estates ...
Preparing Infrastructure for the Next Phase of Agentic AI
Gretchen Stewart | | agent orchestration, agentic AI, AI agents, AI data architecture, AI infrastructure, AI modernization, AI observability, AI resource management, AI workflows, AI workload management, autonomous agents, cloud infrastructure, data movement, federal agencies, government AI, government IT, human-in-the-loop, hybrid infrastructure, infrastructure modernization, multi-agent systems, on-premises AI, public sector AIAgentic AI is changing government infrastructure requirements, pushing agencies to rethink workflows, observability, data movement and resource prioritization before investing in new hardware ...
