June 2026 Summaries

DORA metrics are widely recognized as the industry standard for evaluating software delivery performance, measuring speed and reliability through deployment frequency, lead time for changes, change failure rate, and time to restore service. However, these metrics lack context, failing to address the complexity or quality of the work being produced, which can lead to misinterpretations and misguided actions. Weave offers a solution by providing real-time, AI-powered contextual intelligence that supplements DORA metrics, enabling engineering leaders to understand the underlying factors affecting performance. This platform analyzes the complexity and impact of code changes and reviews, offering insights that allow for informed decision-making and targeted improvements without micromanaging individual engineers. By integrating Weave with existing DORA metrics, organizations can achieve a comprehensive understanding of their engineering processes, facilitating strategic conversations that focus on removing obstacles and enhancing team performance sustainably.

In the ongoing debate between engineering intelligence platforms, Weave and Jellyfish represent two distinct approaches to evaluating engineering team performance, with Weave embracing an AI-first architecture and Jellyfish maintaining a traditional framework. Weave is designed specifically for the AI era, focusing on analyzing the substance of work and attributing contributions to either human developers or AI agents, thus offering a more precise measurement of engineering effort and AI tool adoption. Its approach allows for a detailed understanding of AI's impact on productivity and ROI, making it particularly valuable for teams utilizing AI coding assistants. In contrast, Jellyfish excels at tracking the development process through metrics like DORA and cycle time, aligning engineering activities with business goals, and offering insights for financial reporting. However, it lacks the ability to deeply assess the complexity of work or the contribution of AI-native capabilities. As the market evolves, the choice between these platforms hinges on whether a team prioritizes the detailed analysis of AI-driven outputs or a comprehensive understanding of development processes.

Weave offers a transparent pricing model designed to help engineering teams move beyond traditional metrics to demonstrate the real return on investment of development tools. With a Free plan and a Pro plan costing $50 per engineer each month, Weave emphasizes that the true cost of engineering lies in inefficiencies and developer burnout rather than subscription fees. By adopting the SPACE framework, which evaluates satisfaction, performance, activity, communication, and efficiency, teams can gain a comprehensive view of productivity and uncover hidden bottlenecks, potentially increasing coding speed by 15-35%. The platform's unique Code Output metric, with a strong correlation to true effort, provides valuable insights that surpass those available from traditional tools, leading to a positive ROI within 3-6 months. Weave positions itself as more than just a dashboard, aiming to be a partner in fostering a high-performing and sustainable engineering culture.

The CORE 4 metrics provide a modern and balanced framework for measuring engineering productivity, addressing the limitations of traditional metrics like lines of code or ticket counts. Developed by experts from communities such as DORA, SPACE, and DevEx, the CORE 4 comprises four key areas: Speed, Effectiveness, Quality, and Impact, which collectively offer a holistic view of an engineering team's health. Speed measures the volume of code changes, Effectiveness uses the Developer Experience Index to assess development process friction, Quality evaluates the Change Failure Rate for stable delivery, and Impact gauges the proportion of work on new features versus maintenance. These metrics work in tandem with DORA metrics, which focus on delivery process health, to provide engineering leaders with a comprehensive understanding of team performance, enabling them to address bottlenecks and align engineering efforts with business goals. Adopting the CORE 4 framework alongside DORA can begin with establishing a DORA baseline and then layering CORE 4 for deeper insights, while leveraging analytics platforms like Weave can automate data collection and provide actionable intelligence.

Engineering leaders are increasingly seeking alternatives to GetDX and other traditional engineering intelligence platforms due to challenges in accurately measuring productivity in the AI era. GetDX, originally known for combining developer experience surveys with workflow data, has become less effective post-AI rise, as it struggles to differentiate between AI-generated and human-written code. This limitation is shared by tools like Jellyfish and LinearB, which focus on process metrics but fail to analyze the code's complexity or novelty, leaving a gap in understanding true engineering effort and AI tools' impact. Weave offers a solution with its AI-first approach, using machine learning to analyze code changes and provide a standardized unit of effort, offering a more accurate measure of productivity. Weave's approach, which tracks the content of pull requests to understand complexity and refactoring, provides engineering leaders with a clearer picture of their team's output and the ROI from AI coding tools, distinguishing it from other platforms like GetDX, Waydev, and Axify, which have different focal areas such as business alignment, Git analytics, and team morale.

Navigating the landscape of engineering metrics can be daunting, but understanding the complementary nature of DORA and CORE 4 metrics can provide engineering leaders with a comprehensive view of their team's performance. DORA metrics, developed from the DevOps Research and Assessment program, focus on the health of the software delivery process by measuring speed and stability through metrics like deployment frequency and change failure rate. These metrics serve as indicators of a team's efficiency and resilience in shipping software. On the other hand, CORE 4 metrics offer a holistic perspective by integrating concepts from frameworks like DORA and SPACE to evaluate overall engineering productivity and effectiveness, addressing speed, developer experience, quality, and business impact. By using both frameworks together, leaders can achieve a nuanced understanding of both the technical and human aspects of their engineering operations, allowing them to identify bottlenecks and opportunities for improvement, ultimately connecting software delivery health with developer satisfaction and business outcomes.

Telnyx, an advanced AI-driven engineering organization, distinguishes itself with a private global IP network and five-nines reliability, serving large enterprises while maintaining a diverse codebase. Within Telnyx, engineers primarily supervise bots rather than writing code, with seniority measured by the return on investment generated using AI resources. The company utilizes 1,400 autonomous bots for tasks ranging from code reviews to incident responses, significantly reducing the need for manual intervention. Their AI system includes ADA, a Slack-based AI Dispatcher Agent that automates ticket creation, allowing product managers to focus on vision and prioritization. The engineering structure is divided into product teams and AI factory teams, with the latter enhancing the output of the former. Telnyx is progressing towards fully autonomous deployments where bots independently manage tasks from backlog to production. Notably, the organization emphasizes the importance of compute power over headcount for its top engineers, reflecting a shift in how engineering success is measured. Despite extensive AI integration, Telnyx is actively hiring, underscoring a growing demand for engineers who can effectively communicate intents to bots, thus enhancing productivity rather than reducing employment opportunities.

Many agile teams struggle with accurately measuring and improving their speed and efficiency because traditional metrics like lines of code and story points fail to provide meaningful insights. These outdated measures often lack context, can be manipulated, and do not account for human factors or the impact of AI. Modern engineering analytics, facilitated by Engineering Intelligence Platforms (EIPs), offer a more nuanced approach by integrating with existing tech stacks to diagnose system health and identify bottlenecks. Key analytics categories include DORA metrics for delivery pipeline health, the SPACE framework for developer experience and well-being, and AI adoption metrics to evaluate the effectiveness of AI tools. Tools like Weave and others provide specialized insights into AI's impact and help streamline workflows, ultimately leading to faster and higher-quality software delivery. Selecting the right analytics tool involves aligning with team goals, ensuring seamless integration with the tech stack, and fostering a culture of transparency and continuous improvement.

Engineering leaders face the challenge of selecting and proving the value of developer productivity tools amidst a plethora of options, each claiming to enhance efficiency. Traditional metrics like lines of code are outdated, and modern approaches focus on outcomes such as cycle time, code quality, developer experience, and business impact. A three-step framework is essential for measuring ROI effectively: establishing a baseline of current performance, tracking meaningful metrics post-implementation, and linking improvements to business outcomes. The article categorizes productivity tools into AI coding assistants, engineering analytics platforms, project trackers, CI/CD tools, and communication enhancements, emphasizing the importance of choosing tools that integrate seamlessly into existing workflows and provide actionable insights. By identifying specific pain points and running pilot programs, organizations can make informed decisions that align with strategic goals and ensure genuine improvements in productivity, ultimately leading to a measurable return on investment.

Engineering productivity metrics are evolving in June 2026 as AI coding assistants become standard tools, rendering traditional measures like lines of code and commit counts obsolete. These outdated metrics fail to capture the true value and impact of engineering work, prompting a shift towards AI-driven productivity analytics. This new approach leverages large language models and domain-specific machine learning to deeply analyze the complexity, quality, and business context of code, moving beyond surface-level activity counts. AI-driven insights enable leaders to objectively assess team performance, understand the return on investment from AI tools, and ensure sustainable workflows. Platforms like Weave exemplify this shift by providing real-time visibility into engineering processes, separating human and AI contributions, and benchmarking output to track true productivity and value. This transformation empowers leaders to optimize development processes, improve team health, and effectively measure the ROI of their technology investments in a world where AI is integral to software development.

AI Engineering Analytics Platforms are emerging as essential tools for modern engineering leadership, addressing the inadequacies of traditional metrics like lines of code or commit frequency in assessing productivity and impact in the AI era. These platforms connect with development tools such as Git providers and project management systems to provide real-time, actionable insights through advanced AI and machine learning, focusing on the complexity, risk, and business impact of code rather than mere activity counts. Choosing the right platform involves evaluating deep contextual insights, seamless integration with existing tech stacks, engineer empowerment over micromanagement, and robust security measures. The Weave platform distinguishes itself by focusing on AI attribution and providing a holistic view of both human and AI contributions, enabling leaders to accurately measure AI adoption, calculate ROI, and improve team performance. As AI fundamentally transforms software development, these platforms are no longer a luxury but a strategic necessity for navigating the future effectively.

Engineering teams can greatly benefit from integrating DORA metrics with AI-driven analytics tools to enhance productivity and performance evaluation. DORA metrics, established by DevOps Research and Assessment, provide a standard for assessing software delivery processes by focusing on deployment frequency, lead time for changes, change failure rate, and time to restore service. While these metrics offer valuable insights into the speed and stability of delivery pipelines, they fall short in evaluating the quality and substance of the work being delivered. To address this gap, modern engineering teams are turning to advanced analytics platforms like Weave, which utilize AI to analyze development artifacts, providing a deeper understanding of code complexity, risk, and impact. These tools automate data collection, integrate seamlessly with existing systems, and offer actionable insights beyond what spreadsheets can provide, enabling teams to move beyond simply tracking process efficiency to understanding the actual impact of their work. By combining DORA's foundational process metrics with AI-powered insights, engineering leaders can create a more comprehensive, data-driven strategy that not only measures speed and stability but also assesses the value of the work produced, leading to a more productive and innovative engineering organization.

Weave and Swarmia are two leading engineering analytics platforms that cater to different approaches in understanding team performance—process-centric and output-centric. Swarmia focuses on optimizing the development process and enhancing developer experience through established frameworks like DORA and SPACE, making it ideal for teams aiming to streamline workflows and identify bottlenecks. In contrast, Weave employs AI to provide a deeper analysis of the work itself, assessing the complexity and quality of code beyond traditional metrics. It quantifies engineering output by analyzing pull requests and categorizing work efforts, offering unique insights into the impact of AI tools and the substance of engineering work. The choice between these platforms depends on whether an organization wants to prioritize process efficiency or gain a more nuanced understanding of the value being delivered by their engineering teams.