Agile metrics are key performance indicators (KPIs) that help measure, evaluate, and optimize the efficiency of Agile software development practices, processes, and outputs. They provide visibility into how well Agile teams are delivering value, enabling data-driven decisions, and fostering continuous improvement. Agile metrics sit under the broader umbrella of software engineering metrics, which track code quality, system performance, release velocity, and more.

Good software development teams are focused on outputs, and can bring key metrics to bear that illustrate just what the engineering organization is building on a daily, monthly and yearly basis. Developer productivity is often assessed retrospectively: if the team is hitting DORA metrics, we assume everything in the lifecycle before production is sound. But the best teams dig deeper, and aim to solve the problem backwards as well as forwards by looking at the process as well as the results.

There are many missteps in the app modernization journey. For more than ten years, we’ve worked with clients on hundreds of modernization projects, from single apps to portfolios of apps in large enterprises and our experience has led us to identify four of the most common anti-patterns impacting organizations.

Even the most reliable and well-designed software systems experience failures. Tracking incident response metrics helps teams strengthen both organizational preparedness and system resilience by uncovering trends, gaps, and opportunities for improvement. In short, important metrics for incident management are: Understanding these metrics helps engineering leaders improve service uptime, meet SLAs, and align operational capacity.

In the complex landscape of microservices within Amazon Elastic Kubernetes Service (EKS), maintaining seamless connectivity within microservices is paramount for ensuring optimal performance and reliability. As organizations increasingly embrace microservices architecture, understanding how to observe and troubleshoot microservices connectivity issues in Amazon EKS becomes a critical skill set.

Microservices logging is the practice of tracking and recording the activities of specific services in a distributed microservices architecture. Logging is an important aspect of any software system, and it is more critical for a microservices architecture as there are many small, independent services interacting with each other.

Jellyfish is one of the most popular engineering management platforms, offering comprehensive insights into engineering organizations, their tasks, and operational processes. Engineering management platforms aggregate and analyze metrics from various tools and systems that enable the software delivery process and development lifecycle. Jellyfish and other engineering management platforms aim to connect key development processes and decisions to overarching business goals.

Notifications are designed to be annoying. Think of your phone buzzing in a quiet room: it demands your attention, lighting up your screen and making noise so you look at it. A notification is supposed to pull you away from whatever you’re working on. They can be useful, but they can also be a nuisance.

Microservices architecture is a method of developing software systems that structures an application as a collection of loosely coupled services, each focusing on a single function or business capability. Each service operates within a discrete, confined context, communicating with other services through well-defined interfaces — typically APIs.

In cultures of continuous improvement, the criteria by which teams define a release's fitness for production is flexible by definition. Engineering organizations strive to balance risk and velocity, aiming for high quality releases on a cadence that doesn’t impede overall business throughput.