This article outlines engineering improvements for DevSecOps, cloud, and ML infrastructure to achieve operational resilience. It emphasizes integrating security into CI/CD, improving observability with centralized monitoring, and tightening privileged access, all within a multi-cloud environment to move from reactive troubleshooting to automated, evidence-based operations.
The book explains how DevOps teams can move from scripted automation to agentic AI-driven workflows, emphasizing practical use in CI/CD, infrastructure, and cloud operations, with strong guardrails and governance for production environments.
The document explains how continuous operations in DevOps use automated deployments, health checks, and role-based access to keep services running; it notes the risk of broad admin permissions and the need for in-flight monitoring beyond initial login to prevent destructive commands.
Translated title. Original: Новые плейбуки, старые порядки: почему буксует внедрение IaC / Хабр
The article argues that infrastructure as code is slow to mature in established teams due to persistent manual steps. It presents a practical case where a crisis-driven shift to IaC, GitOps, and formalized processes enables automated, auditable, and scalable infrastructure management from network to applications.
Recent attacks used stolen developer credentials and malicious pull requests to target CI systems and steal secrets. The article recommends separating build and deploy jobs, limiting CI credentials, requiring deployment approval, and pinning actions and images to fixed versions.
The document discusses using automated orchestration (OOS) and AI-assisted tooling to address disk management in Alibaba Cloud's ECS, replacing error-prone manual workflows with a repeatable, tested process. It outlines two validation scenarios, an end-to-end workflow, and a technical architecture that combines Terraform, Python, and OOS to modify disk specifications while ensuring safety and traceability.
The document describes moving CI from cloud to local machines to reduce latency, speed up feedback, and improve iteration with AI agents, by removing setup, network, and coordination overhead and expanding local testing harnesses with standardized tooling.
BMad Method is an open-source toolchain for Agile AI-driven development, pairing explicit decision-making with scalable context across projects. It offers stable and preview releases, Node/Python requirements, and multiple integration plugins to manage planning, implementation, and collaboration.
Agentic code generation lets teams build services faster than organizations can agree on their design, creating hidden architectural debt through locally reasonable but inconsistent choices. The article argues that teams should set shared defaults, add architectural checks, and focus review on system boundaries rather than trying to inspect every generated line.
The article explains using Postman Context Graph with Claude Code to add a new service end-to-end, ensuring downstream dependencies and tests stay in sync across repositories; it highlights the risks of long-context prompts and shows how the graph and plugin skills automate collection creation, testing, mocking, monitoring, and CI integration for a healthcare app.