On-call engineers spend hours manually investigating incidents across multiple observability tools, logs, and monitoring systems. This process delays incident resolution and impacts business operations, especially when teams need to correlate data across different monitoring platforms. AWS DevOps Agent (in preview) is a frontier agent that resolves and proactively prevents incidents, continuously improving reliability and performance of applications in AWS, multicloud, and hybrid environments.
Black Friday is the ultimate moment of truth for the retail sector. The stakes are immense: smooth performance can lead to record profits, but even a brief period of downtime can be catastrophic for any major retailer, resulting in revenue loss and lasting brand damage. As this critical trading period is upon us, customers are looking for expertise to move beyond basic monitoring and achieve greater integration within their IT environment.
Dynatrace connects its observability platform to Amazon Bedrock AgentCore. The integration provides real-time insight into autonomous AI agents within AWS environments. For developers, this means better control over agentic workflows and their performance. Amazon Bedrock AgentCore helps build and deploy AI agents without requiring infrastructure management. The integration with Dynatrace ensures that agent telemetry is converted into insights. Teams can use this to monitor the reliability and responsiveness of agents at the trace level. Intelligent alerts for key metrics become possible.
WordPress powers countless websites across various domains, offering incredible versatility. This Content Management System (CMS) is the undisputed leader in the CMS market, powering an impressive 43.6% of all websites globally, according to these statistics. With over 810 million websites built on the platform and hundreds more launching daily (500+), its adoption continues to surge. This widespread use gives WordPress a massive 62% CMS market share, significantly outpacing its rivals.
From a technical standpoint, the solution relies on a lightweight serverless function (such as an AWS Lambda) that receives GitLab webhooks via an API Gateway endpoint, formats the payload as structured logs, and ships them into Grafana Cloud Logs. Users can then use LogQL queries to analyze CI/CD activity by project, deployment success rates, or build times. Furthermore, these logs can be combined with application performance data in Grafana dashboards, for example, seeing error rates plotted alongside specific deploys or code changes.
Google Cloud has updated its Vertex AI Agent Builder with new observability dashboards, faster build-and-deploy tools, and stronger governance controls, aiming to make it easier for developers to move AI agents from prototype to production at scale. The update adds an observability dashboard within the Agent Engine runtime to track token usage, latency, and error rates, along with a new evaluation layer that can simulate user interactions to test agent reliability.
Devops practices that actually move the needle aren't the flashy ones everyone talks about. It's the unglamorous work, such as standardizing CI/CD pipelines across teams, implementing consistent observability standards, and treating environment alignment as data architecture.
Kubernetes networking is highly flexible but this flexibility can introduce security risks because all pods can communicate with each other by default. Cilium addresses these challenges by providing a modern, high-performance solution for Kubernetes networking that combines security, observability and performance using eBPF. Cilium is an open-source networking and security solution designed for cloud-native environments. It provides high-performance pod-to-pod networking utilizing eBPF and allows identity-aware network policies at the API level, enforcing fine grained controls.
In the first article we looked at the Java developer's dilemma: the gap between flashy prototypes and the reality of enterprise production systems. In the second article we explored why new types of applications are needed, and how AI changes the shape of enterprise software. This article focuses on what those changes mean for architecture. If applications look different, the way we structure them has to change as well.
At ITRS, we make society's critical technology work. Our mission is to deliver automated and holistic IT observability solutions that safeguard critical applications and enable innovation. We are the only monitoring and observability platform designed for the most demanding and regulated industries - trusted by 90% of Tier 1 capital markets firms. We believe when our team thrives, so do our customers.
Advanced debug logging is the cornerstone of high-performance applications. Whether working in cloud-native, microservice or monolithic architecture, strong debug logging practices enable developers to resolve problems, maintain system health and support scalable operations. To succeed in today's fast-paced environment, development teams need modern logging strategies, refined best practices and resilient error-handling techniques. Debug logging refers to the internal operation of an application, generating detailed messages that detect variable states and execution branches.
Observability directly improves system stability and availability. The survey revealed that 75% of businesses report a positive return on their observability investments. Nearly one in five (18%) say they are realizing a 3-10x return on investment. For businesses, the top benefits of observability are: Reduced unplanned downtime (55% of leaders) Improved overall operational efficiency (50%) Reduced security risk (46%) Engineering Efficiency Observability also improves engineering productivity and satisfaction by reducing the time engineers spend on reactive tasks.
Vercel has rolled out the AI Gateway for production workloads. The service provides a single API endpoint for accessing a wide range of large language and generative models, aiming to simplify integration and management for developers. The AI Gateway allows applications to send inference requests to multiple model providers through one endpoint. It supports bring-your-own-key authentication, which means developers can use their own API keys from providers such as OpenAI, Anthropic, or Google without paying an additional token markup.
MCP provides a structured interface for tools and AI agents to query observability data, enabling developers to surface traces, metrics, and logs in their IDE without switching context. This high-level capability is designed to streamline debugging and reduce time spent navigating between systems, while also laying the groundwork for consistent access across AI assistants and integrations. Originally launched as an open source project, MCP is now offered by Honeycomb as a managed service.
In modern, distributed systems, that work is harder than ever. Services depend on other services, deployments happen constantly, and small failures ripple into larger ones. While teams scramble to piece things together, customers are already feeling the impact, and the business is losing money by the minute. For years, the industry used $5,600 per minute as the average cost of an outage as was suggested by Gartner in 2014.
Log rotation using copy truncate mode can cause log loss or duplicate collection because of non-atomic operations and new file creation. The copy action introduces risks of misidentification by collectors.
