Contributed Content
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 ...
Three Key Lessons from the Recent AWS and Cloudflare Outages
Misbah Rehman | | AI-powered monitoring, blast radius reduction, cascading failures, cloud infrastructure resilience, cloud reliability engineering, cloud risk management, Disaster recovery testing, DNS failures, enterprise cloud strategy, high availability architecture, hyperscaler failures, infrastructure automation, multi-cloud architecture, multi-region failover, SRE best practicesRecent AWS and Cloudflare outages reveal how single subsystem failures can cascade globally. Learn key lessons on multi-cloud resilience, AI-powered monitoring, and disaster recovery ...
The Self-Inflicted Outage: When “Too Big to Fail” Meets the Reality of Hyperscale Complexity
Michael Smith | | automation risk, cascading failures, certificate lifecycle management, cloud outage causes, cloud reliability, cloud resilience, devops automation, distributed systems reliability, failure design, hyperscaler failures, infrastructure as code, infrastructure complexity, multi-cloud resilience, operational risk, PKI automation, scaling limits, self-inflicted outages, SRE best practicesModern cloud outages are increasingly caused by automation, configuration errors, and hidden design limits. Learn how to build resilience ...
3 Notable Software Development Trends for 2026 and Beyond
Roman Davydov | | AI in SDLC, AI-enabled development, citizen developers, code generation AI, DevSecOps trends, IT talent strategy, Low-code development platforms, software delivery optimization, software development trends 2026, software engineering trends, software testing automation, talent density maximizationExplore the key software development trends for 2026, including AI-enabled development, low-code platforms, and talent density maximization shaping modern SDLCs ...
Rethinking Software Architecture for the Next Generation of User Interfaces
The next wave of user interfaces demands a complete rethinking of software architecture. For decades, digital products have relied on menus, checkboxes, search bars, and forms. These elements worked, but they were ...
Designing for Failure: 4 Resilience Practices That Make Outages Boring
Mihir Vora | | canary deployments, Designing for failure, resilience engineering, Resilient Software Systems, SRE best practicesLast winter, my city Richmond VA suffered water distribution outages for days after a blizzard. Not because of one big failure, but because backup pumps failed, sensors misread, alerts got buried, and ...
Why Privacy-Safe Logging Remains One of the Hardest Problems in DevOps
Vignesh Sundaram | | alert fatigue, cloud-native security, devsecops, observability, PII redaction, privacy-safe loggingAs cloud-native architectures scale and regulatory pressure intensifies, organizations are finally recognizing that their logging pipelines contain sensitive. Logs fuel observability, debugging, compliance investigations, and incident response, yet they also remain one ...
AI and ML in DevOps: Transforming CI/CD Pipelines Into Intelligent, Autonomous Workflows
Arun Goyal | | AI DevOps transformation, AI for cloud infrastructure, AI in DevOps, AI infrastructure management, AI-based security monitoring, AI-driven CI/CD pipelines, AI-driven DevSecOps, AI-powered CI/CD, AIOps, automated incident response, Autonomous DevOps, Autonomous software delivery, DevOps automation using AI, How AI transforms DevOps, Intelligent DevOps automation, Intelligent test automation, Machine learning DevOps, ML in DevOps pipelines, ML-based anomaly detection, Next-generation DevOps trends, Predictive analytics in DevOps, Predictive maintenance DevOps, Self-healing systems DevOps, Smart monitoring with AIOps, Zero-touch infrastructureDiscover how AI and ML are revolutionizing DevOps through intelligent automation, autonomous CI/CD pipelines, AIOps, enhanced security, predictive analytics, and self-healing systems. Learn why AI-powered DevOps is essential for modern enterprises ...
Agentic AI in Observability Platforms: Empowering Autonomous SRE
Neel Shah | | agentic AI Kubernetes, agentic AI observability, AI log metric trace correlation, AI root cause analysis, AI-driven incident response, AIOps agentic AI, autonomous DevOps agents, autonomous remediation, autonomous SRE agents, intelligent incident triage, MTTR reduction with AI, observability automation, OpenTelemetry workflows, proactive resilience AI, self-healing Kubernetes, SRE automation toolsAgentic AI transforms observability by using autonomous agents to analyze telemetry, detect issues, and execute fixes—cutting MTTR and eliminating manual SRE toil ...
Code Quality and Security Risks of AI-Generated Code
Joydip Kanjilal | | AI attack surface, AI code security, AI coding assistants, AI development tools, AI model limitations, AI security challenges, AI training data vulnerabilities, AI-generated code, AI-powered development risks, automated code review, ChatGPT code generation, CI/CD security scanning, code quality risks, code security best practices, dependency risks, developer awareness, GitHub Copilot risks, hallucinated code, human-led code review, inefficient algorithms, insecure AI code, insecure code patterns, poor code maintainability, secure coding practices, software development security, spaghetti code, technical debt from AI toolsAI-powered tools and coding assistants have become popular and widely used among software developers for several reasons. They promise to deliver not only speed and comfort but also a tool for filling ...
Enterprise IntelliScope: A Cloud-Native Cognitive Reliability Framework for AI-Driven Incident Management
Hrushikesh Deshmukh | | AI automation for SRE, AI-driven observability, cloud-native monitoring architecture, cognitive observability framework, Enterprise IntelliScope, explainable AI for DevOps, GNN-based dependency mapping, human-in-the-loop governance, hybrid cloud reliability engineering, intelligent observability blueprint, Kubernetes AI observability, MTTR reduction AI, multi-cloud incident management, multi-cloud telemetry platform, OpenTelemetry enhancement, predictive alerting systems, proactive incident prevention, RAG incident analysis, SRE automation framework, unified observability consoleDiscover how Enterprise IntelliScope unifies observability, AI reasoning and human-in-the-loop governance to deliver proactive incident management and reliability at scale ...
Integrating Performance Testing into CI/CD: A Practical Framework
Sudhakar Reddy Narra | | CI/CD performance testing, CI/CD performance thresholds, CI/CD pipeline optimization, continuous performance testing, developer performance feedback, full load testing pre-release, performance budgets, performance regression testing, performance SLOs, performance smoke tests, performance testing tools CI/CD, performance trend analysis, production-like performance testing, reliable load testing automation, shift-left performance testing, tiered performance testing strategyLearn how to integrate continuous performance testing into CI/CD using a tiered strategy that catches regressions early and improves speed, reliability and scalability ...
