May 2026 Summaries

Feature flag tools have significantly evolved from basic on/off toggles to sophisticated experimentation platforms, seamlessly integrating with CI/CD pipelines and observability stacks to enable continuous and controlled software feature releases. These tools allow developers to decouple deployment from release, letting features be shipped in a dormant state and activated when ready, thus eliminating the need for disruptive maintenance windows and enabling features to be rolled out incrementally with real-time monitoring and AI-driven guardrails. The modern feature flag landscape includes platforms like Harness FME, LaunchDarkly, and Statsig, each offering unique capabilities such as unified flags and experimentation, progressive delivery, statistical analysis, and governance features tailored for different team sizes and regulatory requirements. By providing the ability to run A/B tests, monitor feature impacts, and manage flags lifecycle, these tools empower teams to conduct safer releases, reduce technical debt, and align software development directly with business KPIs, making shipping features a confident and data-driven process rather than a gamble.

The integration of generative AI in software development is fundamentally reshaping the role of developers, transitioning them from primary code authors to validators of AI-generated output, which has introduced new challenges in measuring productivity. Traditional productivity frameworks, which focus on metrics like cycle time and business outcomes, are inadequate for capturing the nuances of AI-related work such as validation, cognitive load, and trust calibration, leading to a disconnect between perceived and actual productivity gains. This shift has intensified the developer crisis, as organizations struggle to account for invisible overheads like increased code review and debugging time, which are not reflected in current metrics. The discrepancy between management's perception of AI's impact and the developers' experiences highlights the need for more comprehensive measurement systems that incorporate the complexities of AI-driven workflows. Building trust and establishing clear policies around data usage are crucial for creating effective measurement systems that align with the realities of modern software development.

The text discusses the challenges of addressing security vulnerabilities in the AI era, emphasizing that current security practices are inadequate for the speed and scale of AI-driven exploitation. It argues that the division of responsibilities between security and engineering teams is outdated and ineffective, as security teams can identify issues but lack the authority to implement technical solutions. The author suggests that a collaborative approach, where security and engineering jointly manage threat assessments, remediation plans, and accountability, is necessary to keep pace with evolving threats. The text warns that organizations that fail to adapt to this integrated model risk severe consequences, as AI has drastically reduced the time required for exploiting vulnerabilities. The piece highlights the urgency for organizations to proactively redefine roles and processes to mitigate risks effectively, stressing that the moment for change is now.

Google Cloud BigQuery has become integral to modern data platforms, facilitating analytics, AI, and large-scale reporting architectures, yet managing schema changes within it remains challenging. Harness has introduced BigQuery support in its Database DevOps platform, aiming to bring automation, governance, and reliability to BigQuery deployments, similar to application DevOps. This integration allows teams to manage schema changes with pipeline-driven workflows, automate deployments, version control changes alongside application code, and enforce governance policies using secure OIDC-based authentication for keyless access. By connecting directly to BigQuery environments via JDBC and using Harness pipelines, organizations can improve collaboration, streamline release processes, and enhance security without compromising delivery speed. This development addresses existing challenges in managing BigQuery changes, such as manual and fragmented deployment processes, limited change visibility, and inconsistent workflows, thereby enhancing the overall security, compliance, and operational reliability of database management in analytics workflows.

Harness has integrated AI cost management into its existing Cloud & AI Cost Management (CACM) platform to provide companies with comprehensive visibility and control over their AI expenditures, which are rising as AI features rapidly transition from prototype to production. This solution addresses the challenge of understanding and justifying AI spending by offering granular cost visibility and allocation across various providers and services, tying every dollar spent to specific AI agents, sessions, and business outcomes. The platform leverages unit economics to transform AI invoices into investment decisions, enabling organizations to evaluate costs on a per-outcome basis, such as cost per resolved ticket or completed workflow. By unifying AI and cloud cost management, Harness allows teams to monitor and analyze AI spending using the same financial operations (FinOps) principles already applied to cloud costs, with features like anomaly detection and budget governance to preempt cost overruns.

AI coding tools like Cursor, Claude Code, Copilot, and Windsurf have significantly accelerated code generation, transforming the software development landscape by reducing workflow times from hours to minutes. Despite the increase in AI-generated pull requests and the volume of code produced, many organizations struggle to quantify the actual output and return on investment of these AI-assisted developments. Traditional engineering systems, which begin with a commit, are ill-equipped to measure the impact of AI, as development now starts with a prompt. This shift has led to the creation of AI DLC Insights by Harness, which provides comprehensive visibility into AI-generated work across the entire development lifecycle, from the initial prompt to production. This tool helps organizations identify productivity, efficiency, and the quality of shipped products by tracking AI adoption, token consumption, and production outcomes. It aims to bridge the gap between AI investment and engineering output, ensuring that AI adoption translates to better, faster, and more efficient software delivery.

Gartner predicts a significant increase in AI software spending, reaching $2.59 trillion by 2026, yet many organizations struggle to measure the ROI of their AI investments. Harness has introduced two products, AI DLC Insights and Cloud & AI Cost Management, to address these challenges by linking AI-generated code to its deployment and tracking AI infrastructure costs. AI DLC Insights helps engineering leaders monitor AI adoption and efficiency by measuring token spend and its conversion into productive work, while Cloud & AI Cost Management offers detailed visibility into AI infrastructure costs, enabling organizations to assess the value of AI agents in production. These tools aim to shift the focus from AI adoption to proving its financial worth, providing a unified platform for evaluating the ROI of AI expenditures.

Harness AI Test Automation offers a seamless integration for Playwright suites, enabling teams to run their existing test projects without any code modifications while incorporating AI-driven failure analysis and direct integration into build and deployment pipelines. This approach addresses the challenges of running Playwright tests reliably at scale by eliminating the need for complex infrastructure management, offering cloud-based execution with parallel workers, and providing AI-powered triage to classify failures and suggest fixes. Unlike external test execution services that focus solely on browser execution at scale, Harness integrates test results natively into CI/CD pipelines, automatically blocking deployments in the event of test failures and offering AI assertions to simplify complex test conditions. By maintaining the integrity of existing tests and offering a path to evolve unstable tests into AI-generated ones, Harness streamlines the testing process and enhances team collaboration with shared visibility and comprehensive commit-to-deploy traceability.

In May 2026, Harness partnered with the Namma MUG community to host its first Database DevOps event in India, focusing on MongoDB and modern database automation practices. The event, held in Bengaluru, attracted engineers interested in automation, CI/CD, Infrastructure as Code, and database migration strategies for modern applications. A key highlight was the introduction of the OSS Native Mongo Executor initiative, which sparked discussions about the integration of MongoDB with DevOps, the comparison of changelog-driven workflows with traditional scripting, and the importance of using native MongoDB tools over traditional relational methods. Attendees expressed a strong interest in learning how to integrate MongoDB into DevOps workflows effectively, and there was a significant demand for beginner-friendly guides, examples, and templates. The event underscored the community's enthusiasm for more streamlined MongoDB operations, emphasizing reliability, governance, and developer velocity, and highlighted the evolving landscape of Database DevOps beyond traditional relational systems.

"The NoSQL Storm" comic series delves into the intricacies of Database DevOps within NoSQL environments, specifically focusing on the challenges associated with schema evolution, distributed system scaling, and operational reliability as applications expand. Highlighting the fast-paced universe of MongoDB and distributed data, the comic explores how automation and CI/CD pipelines contribute to reducing deployment risks and enhancing consistency. It illustrates how Database DevOps practices offer stability and confidence to NoSQL workflows by effectively balancing governance with developer speed in dynamic engineering settings. The work combines real-world scenarios with visual storytelling, providing insights into managing modern NoSQL environments for developers, DBAs, and platform engineers.

Artifact repository sprawl presents a significant challenge in modern CI/CD pipelines, leading to inefficiencies and security vulnerabilities. This fragmentation occurs when different types of artifacts are stored in separate registries, each requiring distinct authentication and access protocols, complicating software delivery and security management. The dispersal of repositories across multiple platforms can delay pipeline execution, increase operational costs, and obscure visibility into what's running in production, making security governance difficult. Unified artifact management, such as Harness Artifact Registry, aims to centralize and streamline artifact storage and governance, supporting multiple package types and enforcing consistent security policies. By consolidating artifact management and implementing a single governance boundary, organizations can enhance security, reduce operational overhead, and ensure that only vetted artifacts reach production environments, thereby mitigating the risks associated with supply chain attacks and improving incident response.

Mini Shai-Hulud, an advanced version of a self-propagating malware, has emerged as a significant threat to software supply chains by compromising high-profile packages across multiple ecosystems, including npm, PyPI, and RubyGems. This worm exploits trusted CI/CD pipelines, stealing sensitive credentials, and spreading through automated mechanisms, making containment challenging. It utilizes obfuscated loaders, staged payloads, and fingerprinting of developer environments to adapt and maximize credential harvesting. The attack bypasses traditional security checks by abusing CI/CD infrastructure, like GitHub Actions, and using provenance verification to distribute malicious packages. The widespread impact of this malware campaign highlights the vulnerabilities in open-source ecosystems, emphasizing the need for robust security measures, such as secure coding practices, strict credential management, and continuous monitoring of dependencies to mitigate future threats.

Continuous integration (CI) costs can significantly increase as engineering teams grow, encompassing more than just cloud expenses. These costs include infrastructure fees and productivity losses due to slow build times and developer wait time. Optimizing CI involves reducing these costs by enhancing build efficiency, minimizing compute usage, and eliminating unnecessary work without hindering development speed. Key strategies for cost reduction include test optimization, intelligent caching, right-sizing infrastructure, and implementing governance controls. Effective test optimization, such as selective test execution and test parallelization, can significantly reduce build times and improve developer productivity. Intelligent caching helps by reusing previous outputs to avoid redundant work, while using cost-effective infrastructure like spot instances and auto-scaling further reduces expenses. Governance controls, including policy enforcement and visibility through metrics, help maintain long-term cost efficiency. Teams that adopt these strategies often achieve a 50 to 75 percent reduction in CI costs while improving development speed and productivity.

In 2026, the choice between Java SE and Java EE has shifted to selecting the appropriate framework or runtime that sits on top of the Java Virtual Machine (JVM), with options like Spring Boot, Quarkus, Helidon, Micronaut, and vanilla Jakarta EE on various runtimes. The shift from the javax.* to jakarta.* namespace is a significant upgrade challenge, with frameworks such as Spring Boot requiring this transition. Modern runtimes have evolved beyond the "heavyweight app server" critique, offering lightweight, native image capabilities that start quickly and run efficiently, making them competitive with other languages like Go and Node.js. In the enterprise Java landscape, mixed frameworks are common, and AI-powered continuous delivery (CD) and GitOps are crucial for managing these environments effectively. The decision on which framework to use is now driven more by factors like deployment targets, team expertise, and operational fit rather than purely technical distinctions, as many frameworks have converged on similar APIs and capabilities.

Context graphs represent a novel approach to understanding and modeling how work is conducted within an organization, differentiating from traditional knowledge graphs by incorporating temporal sequences and behaviors rather than just static relationships. While knowledge graphs focus on the state of entities and their relationships, context graphs delve into the flow and process, answering questions about how tasks and resolutions occur over time. They operate on three layers: the foundational knowledge graph, a personal graph tracking individual actions, and the context graph itself, which aggregates and anonymizes patterns from personal graphs. Unlike process mining, which deals with structured workflows, context graphs handle fragmented data across various tools without a single event log, aiming to create an adaptive model for agents to understand organizational behavior. This model helps agents decide the best course of action in real-time scenarios by offering a probabilistic map of typical workflows, thus filling the gap between static documentation and real-time decision-making. The construction of context graphs relies on deep connectors, a semantic layer for consistent definitions, trace stitching, and hybrid storage solutions that balance structural and semantic access, all while maintaining privacy and adaptability as the organization evolves.

Change Advisory Boards (CABs) are traditionally used in software release processes to ensure safety by gathering experts for reviews, but they often struggle to keep pace with the rapid delivery cycles of modern development. This model, which relies on periodic human approvals, can create bottlenecks, delay deployments, and increase risks due to larger batched releases. Automated release management, as advocated by Harness Continuous Delivery, offers an alternative by embedding governance directly into the pipeline, ensuring consistent, scalable, and objective validation of changes through automated quality gates and policy as code. This approach enables frequent, smaller releases that limit potential issues' impact and improve recovery time, ultimately enhancing both speed and safety without sacrificing governance. By incorporating AI-assisted deployment verification and orchestration for complex releases, automated systems facilitate seamless, independent deployments across diverse environments, thereby reducing interdependent release risks and accelerating delivery velocity.

A study titled "State of Engineering Excellence 2026" explores the complex impact of AI tools on engineering productivity, revealing both increased output and significant challenges. While 89% of engineering leaders report enhanced productivity from AI, 81% also note a rise in code review time, with developers spending about a third of their day on AI-related tasks not captured by traditional metrics. This suggests that while AI boosts productivity, it also shifts focus to unmeasured areas, creating a gap between code generation and actual value delivery. The report highlights a disconnect between leadership's confidence in current metrics and the reality faced by developers, who often fear performance evaluations based on incomplete data. Organizations like Harness are addressing these issues by developing new capabilities to provide a more comprehensive view of AI's impact, emphasizing the need for AI-native measurement systems to sustain productivity gains.

Effective disaster recovery (DR) testing is crucial for organizational resilience and follows a three-phase lifecycle: planning, execution, and review. Many DR programs falter not due to a lack of technology, but because of untested runbooks and unclear ownership. Harness Resilience Testing integrates chaos, load, and DR testing into a single pipeline, allowing teams to identify risks before they become incidents. Common causes of major incidents include mundane issues like ransomware and human error, which lead to predictable fallout such as revenue loss and compliance findings. Regulatory frameworks now demand documented evidence of periodic DR testing. Successful DR testing involves defining clear objectives, executing plans with discipline, and conducting rigorous reviews to ensure continuous improvement. Challenges such as resource constraints, cross-functional engagement, and undocumented dependencies can be mitigated with proper planning and the use of modern tools like Harness, which facilitates integrated testing within existing workflows. A repeatable DR testing program enhances organizational resilience and provides evidence of improvement over time.

Harness Release Orchestration offers a comprehensive solution for managing and automating complex enterprise software releases, which often involve multiple services, teams, and environments. This advanced framework replaces traditional methods such as spreadsheets and manual checklists with a structured, visual approach that enhances process modeling, scheduling, and real-time tracking. By defining reusable release blueprints and automating pipelines, it ensures consistent, auditable, and efficient release processes while maintaining governance through necessary manual sign-offs. The system supports ad hoc releases for unscheduled needs, multi-service and multi-environment deployments, and provides detailed execution reports for compliance. It integrates with existing tools for notifications and offers AI-powered process creation, which swiftly translates natural language descriptions into structured workflows. This orchestration tool ultimately transforms software delivery into a streamlined, repeatable, and transparent process, enabling enterprises to manage releases more effectively and with greater confidence.

In Q1 2026, significant enhancements were introduced to pipeline orchestration, focusing on accelerating development, simplifying validation, and strengthening governance. Key updates include the ability to execute pipelines from Git tags for immutable versioning, AI-assisted policy authoring without Rego expertise, and improved visibility into queued pipelines. The update also introduces a dry-run validation API to catch errors before committing, step-specific failure notifications to reduce alert noise, and Directed Acyclic Graph (DAG) support for complex step dependencies. Enhanced governance features include AI-powered OPA policy authoring and full GitX integration, allowing for scalable policy creation and management. These advancements aim to streamline workflows, improve validation practices, and enhance governance for platform engineering teams, aligning with modern automation scaling needs.

In the first quarter of 2026, the focus is on AI-powered continuous verification, expanded deployment platform support, and GitOps workflow enhancements, aiming to streamline the software delivery process. AI Verify offers intelligent deployment monitoring without requiring baseline data, and performance improvements are made for Windows deployments, while native GitOps notifications enhance enterprise reliability. Azure Container Apps are supported, allowing for serverless container orchestration, and Windows deployments benefit from session reuse and multi-host dynamic targeting to improve efficiency. Additionally, Amazon ECS deployments gain operational flexibility with standalone scaling and scheduled actions, while Terraform deployments are enhanced with automatic security protections for sensitive data. Continuous verification now integrates AI automation to simplify configuration and monitoring, and GitOps workflows receive AI-powered operational management and centralized notifications for improved collaboration and visibility. Overall, these updates aim to reduce configuration overhead, expand platform capabilities, and align with modern software shipping practices.

Harness has introduced significant enhancements to its API Testing platform aimed at simplifying configuration and improving the efficiency of API security testing. The upgrades address common challenges faced by application security teams, such as complex scan setups and inefficient test execution, by streamlining the configuration process and implementing robust validation mechanisms. The new configuration experience consolidates steps into three main sections, provides field-level guidance, and allows for inline creation of dependent entities, reducing the likelihood of errors and the need for expert intervention. Additionally, the platform now includes improved reachability validations to ensure that only properly authenticated and reachable APIs are tested, enhancing the overall quality and reliability of the test output. These enhancements promise a more user-friendly and effective API testing process, helping teams focus on actionable security outcomes without unnecessary delays or resource wastage.

AWS re:Invent 2025 highlighted the shift from theoretical interest in AI to active exploration and implementation within enterprises, emphasizing the role of AI in accelerating software development while exposing existing system weaknesses. The event underscored the necessity of robust engineering practices, governance, and operational models to harness AI effectively, as AI amplifies issues like governance and process inefficiencies rather than solving them. AI's value was noted in reducing mundane tasks, allowing developers to focus on innovative work, while also increasing the importance of governance and security in managing the larger volumes of code AI generates. The discussion also pointed out that AI elevates the need for clear communication and context understanding in organizations, making judgment and context premium skills. Ultimately, AI's integration into enterprises demands a modernization of operational models, where speed and innovation are matched by robust governance and platform engineering, highlighting that the competitive advantage lies in the ability to adapt organizational systems to leverage AI effectively.

Harness has released over 70 new features in April, focusing on enhancing AI-generated code's deployment and management processes. These updates include innovations in the Cursor IDE, which allow developers and AI agents to manage the entire software delivery lifecycle without leaving the editor. Key integrations with Google Cloud and expanded capabilities for security, artifact management, and incident response are also highlighted. These enhancements aim to streamline software delivery by improving feedback loops, automating post-mortem reports, and integrating resilience testing, ultimately reducing the time and effort required for tasks like vulnerability detection and deployment. Additionally, Harness has expanded support for various platforms and tools, including Snowflake, Redshift, BigQuery, and ECS, while introducing features such as AI asset discovery and risk visibility, enhanced security measures, and improved infrastructure as code capabilities.

Google Cloud Next '26 highlighted a transformative shift in software delivery, emphasizing the integration of AI, platform consolidation, and a drive for efficiency that is fundamentally reshaping the Software Development Life Cycle (SDLC). Key discussions at the event revealed a trend toward moving from AI experimentation to operationalization, with organizations seeking to consolidate fragmented tools into comprehensive platforms that embed AI to enhance control, intelligence, and speed. The challenges faced by various industries, such as fragmented CI/CD pipelines and manual release processes, align with core solutions offered by Harness, including software delivery modernization and AI-powered DevOps. The event underscored the urgency of adopting AI-native solutions to streamline workflows, reduce operational bottlenecks, and improve productivity. Harness’s expanded partnership with Google Cloud, integrating AI intelligence across the SDLC, exemplifies this shift, with companies like Keller Williams successfully transforming their software delivery processes. A consistent theme throughout was the need for fewer, smarter tools that integrate security, cost, and delivery, with an emphasis on eliminating complexity.