Скачать или смотреть From AWS Chaos to AI Agents: New DevOps Standards & Cloud Resilience

The tech landscape was dominated recently by a massive AWS outage in the US-East-1 region, which disrupted crucial internet services and underscored a critical mandate: multi-region architecture and non-negotiable observability are essential for modern systems. In response to achieving greater resilience, Slack shared how they advanced their Chef infrastructure by isolating production environments across Availability Zones and building a new EC2 ecosystem called Shipyard, focused on metric-driven rollouts and automated rollbacks.
Organizations are consistently leveling up their platforms, moving through defined stages toward a DevOps maturity model that prioritizes security, automation, and a superior developer experience, often involving infrastructure automation and one-click setup. This pursuit of efficiency is evident in cost-saving migrations, such as Salesforce Commerce Cloud achieving a 40% AWS cost reduction by adopting Amazon Managed Service for Prometheus. Infrastructure optimization continues with solutions like Cilium, which uses eBPF to minimize cross-AZ traffic costs on EKS, and the new Amazon EC2 Capacity Manager, providing a centralized view for monitoring and managing capacity across all AWS regions and accounts.
Monitoring itself is evolving from fragmented tools to unified platforms that drive specific business outcomes. The introduction of Datadog's new OTLP Metrics API now allows for direct ingestion of OpenTelemetry metrics from serverless environments, expanding observability options where collectors cannot be deployed. Resiliency is also improving at the edge with Cloudflare’s new Monitor Groups for Load Balancing, enabling sophisticated, quorum-based health checks for more accurate failover decisions.
The GenAI ecosystem is accelerating development. LinkedIn updated its application stack to support complex AI agents capable of planning and acting, secured by gRPC service schemas and a hybrid observability strategy. AI developers can also leverage new platform features, including the general availability of vector search for Amazon ElastiCache, providing low-latency support for RAG and recommendation engines. Integrating AI securely into workflows is also key, with both OpenAI and Azure DevOps now supporting the Model Context Protocol (MCP), creating a secure local bridge for AI assistants that maintain data privacy and provide real-time project context. Finally, enhancing developer productivity includes tools like GitHub Copilot CLI, which brings AI assistance directly to the terminal for debugging and managing dependencies.
Core platform management continues to demand attention. While Jenkins remains flexible, its enterprise challenges often stem from poor governance, requiring centralized automation and compliance frameworks to ensure scalability. For GitOps, tools like Argo CD are expanding functionality, not only enabling ephemeral environments via the Pull Request Generator but also working with AWS ACK to manage EKS Pod Identities reliably at scale. Even container deployment models are maturing, exemplified by the transition to Azure Functions V2 on Azure Container Apps, which simplifies resource management and unlocks critical features like multi-revision control.

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