Tracy is compatible with Kotlin from version 2.0.0 and Java from version 17. Integrations can be made with SDKs from OpenAI, Anthropic, and Gemini. The library also works with common Kotlin/LLM stacks including OkHttp and Ktor clients, as well as OpenAI, Anthropic, and Gemini ones.
The data that feeds your observability tools is out of control. Too much of it, low quality, unmanaged, and growing faster than anyone budgeted for. When they started building Sawmills two years ago, this was already a serious pain point. Costs were climbing. Signal-to-noise was degrading. Teams were drowning in telemetry that told them less and less while costing more and more.
"A central issue here is the fact that, as systems scale, telemetry scales even faster," explained Azulay. "Every service creates metrics. Every request generates traces.... and logs multiply as the velocity of deployment increases. This is the structural reality of distributed systems." He points to research from Omdia that suggests organisations consistently "under-instrument" their environments, not because they lack the tools to do so, but because they can't afford to fully use them.
The Old Way (Siloed Tools): The application team opens their APM tool. They see slow transaction times but no obvious errors in their code. They create a ticket for the infrastructure team. The infrastructure team checks their dashboards. Server CPU and memory look fine. They blame the network. The network team checks their monitoring tools. Bandwidth is normal, and latency is low. They declare, "It's not the network!" Hours, or even days, are lost in a painful cycle of finger-pointing while the business loses revenue.
The real cost of poor observability isn't just downtime; it's lost trust, wasted engineering hours, and the strain of constant firefighting. But most teams are still working across fragmented monitoring tools, juggling endless alerts, dashboards, and escalation systems that barely talk to one another, which acts like chaos disguised as control. The result is alert storms without context, slow incident response times, and engineers burned out from reacting instead of improving.
Running a global observability platform means one thing above all: your infrastructure must never go down. When you're responsible for monitoring thousands of customers' applications 24/7, network failures aren't just inconvenient, they're existential threats. At New Relic, hundreds of clusters run on multiple clouds, and regions. These clusters depend on a complex web of network connections: regional transit gateways, inter-regional hubs, and cross-cloud links.
Dynatrace has launched Dynatrace Intelligence, a system that combines deterministic AI and agentic AI. The platform is designed to help organizations transition from reactive to autonomous operations. Dynatrace Intelligence is the new agentic operations system that takes center stage at the observability company's Perform conference. It is built to observe and optimize dynamic AI workloads. The platform is designed to enable organizations to build more resilient applications and improve customer experiences.
Lead without authority. You may not have direct reports, yet you shape architecture, quality and the roadmap. Your leverage comes from artifacts, reviews and clear standards, not from title.I started by publishing a lightweight architecture template and a rollout checklist that the team could copy. That reduced ambiguity during design and cut review cycles by nearly 30 percent
I once transitioned from a SaaS CTO role to become a business unit CIO at a Fortune 100 enterprise that aimed to bring startup development processes, technology, and culture into the organization. The executives recognized the importance of developing customer-facing applications, game-changing analytics capabilities, and more automated workflows. Let's just say my team and I did a lot of teaching on agile development and nimble architectures.
AI is no longer a research experiment or a novelty in the IDE: it is part of the software delivery pipeline. Teams are learning that integrating AI into production is less about model performance and more about architecture, process, and accountability. In this article series, we examine what happens after the proof of concept and how AI changes the way we build, test, and operate systems.
The more attributes you add to your metrics, the more complex and valuable questions you can answer. Every additional attribute provides a new dimension for analysis and troubleshooting. For instance, adding an infrastructure attribute, such as region can help you determine if a performance issue is isolated to a specific geographic area or is widespread. Similarly, adding business context, like a store location attribute for an e-commerce platform, allows you to understand if an issue is specific to a particular set of stores
In 2025, nearly every security conversation circled back to AI. In 2026, the center of gravity will shift from raw innovation to governance. DevOps teams that rushed to ship AI capabilities are now on the hook for how those systems behave, what they can reach, and how quickly they can be contained when something goes wrong. At the same time, observability, compliance, and risk are converging.
Dynatrace started collecting trace data from applications in 2005. Organizations wanted to know why an application was slow and what was happening exactly. That first generation was mainly manual and technical. "It was about collecting data and understanding what was going on," explains Spitzbart. APM has remained the company's foundation for a long time and remains a core component today.
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.
However, this change has come with some difficulties, since all our business information is stored online there has also been a spike in criminals who want to get profit out of stealing said information or preventing business operations. Just in 2024, the FBI has reported over $16.6 billion in losses related to cybercrime, and this value is only increasing year over year making that an "observable" environment must also be a "secure" one.
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.
I'm Martha. I'm a product engineer at a company called incident.io. We build a product that handles your end-to-end incident management. That means your alerts firing, paging you, all the way through to writing a postmortem. I work across the stack, but I focus a lot on the reliability of our product and the observability that enables that. Today you're going to leave with a process to unsoup your dashboards, which I promise is a very technical term.
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.
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.
