Contributed Content
Why API Test Generation Is a Judgment Problem, Not a Code Generation Problem
Abhishek Saikia | | adaptive test coverage, AI API testing, AI quality engineering, AI test generation, API test generation, API testing, Automated testing, cross-field testing, DevOps Testing, fine-tuning, LLM testing, QA automation, RAG, software testing, test assertions, test coverageWhen we started using large language models for API test generation at KushoAI, the results were impressive on the surface. Tests appeared in seconds. Coverage breadth went up. The team was excited ...
Why Cryptographic Inventory Is the First Step Toward Quantum Readiness
Michael Carter | | CBOM, CI/CD security, crypto-agility, cryptographic dependencies, cryptographic discovery, cryptographic inventory, cryptography bill of materials, devops security, devsecops, Kubernetes security, ML-KEM, NIST PQC migration, post-quantum cryptography, PQC, quantum readiness, Software Supply Chain SecurityPost-quantum readiness starts with visibility. DevOps teams need a continuous cryptographic inventory to map algorithms, keys, certificates, libraries, infrastructure and third-party dependencies before PQC migration begins ...
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 ...
How to Avoid Repeating the “Automate Everything” Mistake Due to AI FOMO
Alexey Golubev | | AI engineering, AI FOMO, AI Governance, AI implementation, AI in DevOps, AI maturity model, AI productivity, AI ROI, AI SDLC maturity, AI strategy, AI tools, AI-Driven DevOps, artificial intelligence, Cursor, DevOps AI adoption, devops automation, GitHub Copilot, Kubernetes AI, software development lifecycleThe DevOps community has already experienced technological extremes. You might recall the time when automation was thought of as a panacea for all possible engineering issues, and the slogan “Automate Everything” was ...
Why Self-Healing Tests Need a Deployment Gate
Suneet Malhotra | | artificial intelligence, CI/CD, devops, Self-Healing Automation, software testing, test automationWhen an end-to-end test fails after a front-end change, the repair often looks routine. A class name changed. A button moved. A selector that used to be unique now matches two elements ...
Are LLMs Equally Good (or Bad) at Building Secure Software?
Pieter Danhieux | | agentic AI, AI agents, AI code security, AI coding assistants, AI coding costs, AI coding models, AI Development, AI model benchmarking, AI-generated code, application security, Developer Security, framework security, LLM security, OWASP Top 10, SAST, secure code generation, secure coding, software development, software security, token costsAn SCW study finds a large variance in how frontier and budget LLMs perform across different frameworks, and explains the striking cost variance ...
What the Microservices Era Can Teach Us About AI
Mark Fussell | | agent identity, agent observability, agent runtime, agentic AI, agentic durable execution, AI agents, AI architecture, AI Governance, AI infrastructure, AI security, autonomous agents, cloud-native AI, distributed systems, durable execution, LLM observability, microservices, Open Standards, platform engineering, vendor lock-in, workflow orchestrationAI agents are not just microservices with LLMs attached. Their long-running, non-deterministic workflows demand durable execution, per-step identity, governance and observability ...
Is Your New DevSecOps Tooling Reducing Work Or Just Adding to It?
Yogesh Tatwal | | CI/CD, devsecops, SAST, Security scanning, software delivery, Software Supply ChainSecurity belongs in the software delivery pipeline. The harder question is where, how often and at what cost. Many pipeline teams eventually add security scanning to CI/CD, and relatively few go back ...
Treat Business Workflow Changes Like Deployments
Marcelo Cedeño | | business automation, change management, CI/CD, devops, release management, workflow automationBusiness automation often reaches production without the release discipline applied to application code. A routing rule changes, an approval threshold moves, or an integration starts writing to a new system. The edit ...
Developer Resistance to AI Isn’t Fear – It is Identity
Lukasz Lazewski | | 1000x developer, agentic coding, AI adoption, AI agents, AI code review, AI coding tools, AI Orchestration, AI transformation, AI-assisted coding, AI-generated code, coding with AI, developer accountability, developer experience, developer identity, developer motivation, developer productivity, developer resistance to AI, developer trust in AI, developer workflow, Engineering leadership, Future of Programming, human-AI collaboration, software architecture, software developers, software engineeringDeveloper resistance to AI is less about job loss than a deeper shift from hands-on coding to supervising, validating and orchestrating AI-generated work ...
Production-Safe Testing: The Missing Piece in Most DevSecOps Strategies
Dharmesh Acharya | | application security, continuous security, devsecops, Production-Safe Testing, security testingMost DevSecOps teams invest heavily in security before deployment, yet attackers target the production environment where applications, APIs, and user behavior are constantly changing. If security validation stops before release, critical risks ...
Reducing MTTR: A Practical Guide to Correlating Incidents with AIOps
Neel Shah | | AI for SRE, AI observability, AI-driven incident correlation, Aiden AI Copilot, Aiden for SRE, AIOps, alert correlation, alert fatigue, alert storms, automated remediation, automated root cause analysis, devops, Distributed Tracing, grafana, incident correlation, incident management, incident response, Jaeger, Loki, mean time to resolution, metrics logs traces, mttr, observability, ObserveNow, on-call automation, pattern-based correlation, Prometheus, root cause analysis, service dependency mapping, SRE automation, telemetry correlation, topology-aware correlationAI-driven incident correlation helps SRE and DevOps teams reduce alert noise, identify root causes faster and improve MTTR by connecting related metrics, logs and traces ...
