March 2026 Summaries

Harness Blog offers a comprehensive overview of various advancements and features in the DevOps and software delivery space, emphasizing the integration of AI and automation to streamline processes. Key highlights include the introduction of AI-powered tools for database migration and resilience testing, the enhancement of developer productivity through the new Harness Code Experience, and the enterprise-grade scaling of GitOps with Argo CD. Additionally, Harness's platform innovations are recognized in the industry, as evidenced by its leadership position in the 2025 Gartner Magic Quadrant for DevOps platforms. The blog also covers topics such as Infrastructure as Code Management (IaCM), chaos engineering, and cloud cost management, underscoring the company's commitment to advancing technology that bridges gaps between different operational domains, such as finance and engineering.

An offensive security AI agent managed to breach McKinsey's Generative AI platform, Lilli, in under two hours by exploiting existing application security gaps, API misconfigurations, and AI-layer vulnerabilities, rather than using a novel zero-day exploit. The AI agent discovered numerous unauthenticated API endpoints, exploited a SQL injection flaw, and escalated privileges to access a vast amount of sensitive data, including internal chat messages, files, and user accounts. This incident highlights the amplified risk that AI systems pose due to their ability to rapidly exploit interconnected security weaknesses across application, API, and AI layers. The breach underscores the need for organizations to rethink their AI security strategies, emphasizing unified monitoring and response platforms that correlate signals across different technology layers to prevent multi-stage attacks. It serves as a stark reminder that AI not only exposes existing vulnerabilities but also necessitates a shift from segmented security tools to integrated platforms for effective protection.

Snowflake's integration into Harness DB DevOps is transforming how teams manage data warehouse changes by providing a pipeline-driven, automated approach that mirrors application DevOps practices. This integration allows for version control, automated testing, and consistent promotion of schema changes across environments, enhancing both speed and reliability of data platform updates. By incorporating governance features such as approval gates and role-based access controls, Harness ensures safe production changes while offering full visibility and auditability of deployments. As organizations increasingly rely on data-driven applications, this approach helps reduce deployment risks, improve collaboration between developers and data teams, and standardize release processes. Snowflake support in Harness DB DevOps is now available, enabling teams to configure database connections, add change scripts, and create pipelines for seamless and confident releases.

A financial services company successfully deploys code to production 47 times daily across over 200 microservices by employing advanced regression testing rather than reducing test frequency. This approach involves intelligent test selection, process parallelization, flaky test detection, and scalable governance, integrating these elements within a Continuous Integration (CI) framework powered by machine learning to detect and roll back deployment anomalies swiftly. Regression testing is crucial in CI/CD pipelines to ensure that new code changes do not disrupt existing functionalities, especially in complex microservices environments where a small change can have far-reaching impacts. The distinction between regression testing and retesting is emphasized, with the former safeguarding overall application stability and the latter focusing on specific bug fixes. The text outlines strategies for effective regression testing, including smart timing, targeted testing, and the use of parallelization and automation to prevent bottlenecks while maintaining comprehensive verification. In addition to technical execution, the importance of governance and compliance, particularly in regulated environments, is highlighted, illustrating how policy-as-code and audit logging can meet strict regulatory requirements. The text underscores the transition of regression testing from a potential bottleneck into an automated, intelligent safety net, advocating for its integration as a seamless part of the deployment workflow to enable rapid, reliable software delivery.

Harness is addressing the gap between AI-assisted development and the release process by introducing five new capabilities to streamline continuous delivery, facilitating more seamless and confident code releases. While AI coding assistants have accelerated code generation, the release process has not evolved at the same pace, resulting in increased deployment issues and extended mean time to recovery. With the new capabilities, including AI-powered verification and rollback, database DevOps, and warehouse-native feature management, Harness aims to transform the release process from a series of handoffs to a systemized and automated approach. This shift emphasizes consistent and repeatable releases, mitigating the bottleneck created by the rapid pace of AI-driven development. By integrating these capabilities into their continuous delivery platform, Harness enables teams to manage multi-team releases, verify health in real-time, and experiment with feature exposure, ultimately enhancing release reliability and scalability.

In August 2023, HashiCorp's decision to change Terraform's license from an open-source MPL to a more restrictive Business Source License (BSL) has transformed the operational dynamics for many organizations that relied on Terraform for infrastructure automation, creating a risk of vendor lock-in. Previously seen as a community-driven, cloud-agnostic tool, Terraform's new licensing terms have raised uncertainty about future restrictions and feature availability, prompting organizations to reconsider their dependency on it. To mitigate this risk, the text suggests strategies such as migrating to OpenTofu, which is an MPL-licensed fork compatible with Terraform, and adopting Harness Infrastructure as Code Management (IaCM) for vendor-neutral management. These approaches emphasize infrastructure as code (IaC) portability by decoupling operational workflows from tool-specific patterns, allowing for greater flexibility and control over infrastructure automation. The goal is to ensure strategic control and avoid being locked into a single vendor's roadmap, which could lead to technical debt and operational limitations.

Warehouse Native Experimentation, introduced by Harness, revolutionizes the software delivery model by allowing teams to run experiment analyses directly within data warehouses, avoiding the need to export data to separate analytical systems. This capability promotes a seamless integration of deployment, feature management, and experiment measurement, leveraging trusted business data for more reliable and transparent results. By keeping experimentation close to the source of truth, it reduces operational drag, enhances metric credibility, and aligns product, engineering, and data teams around consistent definitions. This approach supports the "release fearlessly" paradigm by closing the loop from deployment to decision-making, ensuring that software changes are not only deployed safely but also evaluated accurately and efficiently.

Harness Release Orchestration addresses the challenges faced by organizations needing rapid software releases, particularly the coordination bottlenecks between automated CI/CD pipelines. In an era where AI has made code generation faster and cheaper, the release management process still relies heavily on manual coordination, leading to inefficiencies and delays. Harness introduces a standardized, scalable solution with features like visual release blueprints, a shared release calendar, and AI-driven process ingestion, aiming to streamline and modernize the release process. By integrating release orchestration directly with continuous delivery, Harness eliminates the integration overhead of separate tools and enhances visibility and governance for stakeholders across engineering, product, and marketing teams. This approach transforms the role of release engineers from mere coordinators to architects of the release process, leveraging AI to ensure safe and efficient software delivery.

Harness Feature Management & Experimentation (FME) has integrated pipelines and policy support to improve release operations, addressing the gap between faster code creation through AI and manual, inconsistent release systems. This integration allows platform teams to streamline workflows by standardizing rollout stages, automating approvals, and embedding cleanup processes, thus reducing reliance on disconnected tools and tribal knowledge. The system operationalizes feature flags by decoupling deployment from release, enabling gradual exposure, experiment management, and rollback without redeployment. Policy as Code further enhances governance by enforcing standards automatically, preventing rollout risks, and maintaining flexibility across teams while avoiding bottlenecks. By consolidating delivery automation, feature release control, and governance within one platform, Harness eliminates toolchain sprawl and improves release safety and efficiency, making it a comprehensive solution for modern software delivery challenges.

Eight years ago, Continuous Verification (CV) was introduced to alleviate the burden on engineers of manually monitoring deployments, offering automated decision-making based on real-time data. While it effectively reduced the need for engineers to monitor dashboards at inconvenient hours, the complexity of configuring CV became burdensome, as teams had to identify relevant metrics and log patterns themselves. The introduction of AI Verification & Rollback addresses this by automating the identification of crucial metrics and patterns at runtime without pre-configuration, providing clear reasoning and plain-language summaries of its analyses. This AI-driven system integrates seamlessly into existing pipelines, enhancing the deployment process by reducing the operational overhead traditionally associated with CV setup. While CV remains valuable for certain use cases, the AI Verification & Rollback offers a more streamlined approach, especially beneficial for teams new to verification or seeking to minimize configuration challenges.

In March 2026, a significant security breach known as the TeamPCP exploit exposed vulnerabilities in CI/CD pipelines that utilize open execution models, where third-party code runs with full privileges. The attack compromised GitHub Actions, allowing the attackers to turn Trivy, a widely used vulnerability scanner, into a tool for harvesting credentials like AWS tokens and SSH keys. This incident, tracked as CVE-2026-33634, affected over 10,000 workflows and highlighted the inherent risks of mutable tags and third-party code execution in open pipelines. The breach spanned various ecosystems, leading to widespread data exposure and demonstrating the need for more secure governed execution pipelines, like those provided by Harness, which control execution through policy gates, customer-owned infrastructure, and scoped credentials. As the industry moves towards more automated and AI-driven processes, the importance of secure pipeline architectures that limit credential exposure and enforce strict execution controls becomes increasingly vital to prevent similar attacks.

The text discusses the advantages of using self-service sandbox environments powered by an Internal Developer Portal (IDP) to streamline software development processes. By automating sandbox provisioning with built-in guardrails, developers gain rapid access to secure, policy-driven environments, enhancing their productivity and reducing reliance on manual requests. This approach not only accelerates development and improves security but also allows platform teams to maintain governance without becoming bottlenecks. The text emphasizes the importance of treating sandbox environments as disposable to catch configuration errors early, and highlights the use of a tiered strategy for different testing needs, such as lightweight developer sandboxes and full-copy sandboxes. It also explains how automated environment provisioning can prevent governance issues and reduce costs by leveraging features like policy-as-code enforcement and CI/CD integration. With these tools, organizations can ensure faster, safer software delivery, while tracking key metrics to demonstrate the return on investment to leadership.

Load testing is crucial for ensuring the stability, speed, and scalability of systems in today's digital economy, where performance issues can significantly impact revenue and reputation. It involves simulating real-world user traffic to identify bottlenecks, validate service-level agreements, and verify that systems can handle expected and peak loads. This guide discusses various types of load testing, including functional, performance, endurance, and scalability testing, each serving distinct purposes such as checking core functions, measuring response times, and validating scaling plans. It emphasizes the importance of starting load testing early in the development process and using realistic data to mirror production conditions. The guide also highlights the integration of AI in load testing, which allows for more dynamic, context-sensitive testing scenarios and proactive optimization of system configurations. Popular tools for load testing include Apache JMeter, k6, Gatling, and Locust, while best practices focus on continuous testing, monitoring key performance indicators, and combining load testing with chaos engineering to ensure resilience under both stress and failure conditions. Overall, load testing transforms uncertainties into actionable insights, enabling teams to deliver robust digital experiences confidently.

Over the past decade, the rapid evolution in enterprise technology has led to the frequent emergence of new paradigms such as DevOps, cloud-native infrastructure, and artificial intelligence (AI), requiring organizations to operationalize these advancements efficiently. Product Portfolio Management (PPM) becomes crucial in managing the business aspects of these paradigms, with resource management being a key component. Harness's Professional Services team exemplifies this by conducting a Job Task Analysis (JTA) to develop a framework for scaling implementation experts. This framework underscores the importance of both hard and soft skills in technology adoption, where hard skills address technical questions and soft skills facilitate stakeholder management and problem-solving. As AI becomes a new frontier similar to previous innovations like Kubernetes, the fundamentals of innovation versus control remain essential, highlighting the necessity of consensus-driven approaches. Harness, leveraging its platform and expertise, aims to guide organizations in successfully adopting these new paradigms through comprehensive service offerings, blending technical credibility with strategic alignment across various sectors.

On March 24, 2026, a significant supply chain attack targeted the AI open-source ecosystem through the Python package LiteLLM, affecting versions 1.82.7 to 1.82.8. The attackers compromised the PyPI distribution pipeline to embed a multi-stage payload that stole credentials and executed remote code, exploiting Python's .pth file mechanism for persistent execution. This attack introduced a complex execution chain that included blockchain-based command-and-control systems and cross-language execution pivots to evade detection, significantly impacting AI applications by exposing sensitive information like API keys and cloud credentials. The attack underscores the need for rigorous monitoring of dependencies and real-time security measures to mitigate risks in AI infrastructure, emphasizing the importance of tools like Harness Supply Chain Security (SCS) for detecting and containing compromised packages before they affect production environments.

The narrative around Artificial Intelligence (AI) has evolved from the "magic box" illusion to a complex system integration challenge, requiring more than just deploying models through APIs. Modern AI deployment in 2026 involves integrating a comprehensive stack that includes models, prompts, data pipelines, agents, and guardrails into production environments to power real user workflows. This shift has resulted in increased complexity and delivery bottlenecks, as traditional CI/CD pipelines designed for deterministic systems struggle to handle AI's non-deterministic nature. The multi-layered AI stack demands integrated release orchestration to prevent fragile and slow deployments. Effective AI deployment requires treating prompts and configurations as code, employing semantic evaluation, progressive rollout strategies, and robust guardrails for safety, compliance, and cost-efficiency. The future of AI deployment emphasizes unified release management over siloed operations, enabling organizations to deploy sophisticated systems safely and efficiently.

Harness CI introduces branch-scoped build sequence IDs to address the limitations of traditional global build counters used in Continuous Integration (CI) systems. By enabling each branch to have its own incrementing counter, this feature eliminates gaps and confusion in build numbering that often arise when different branches increment the same global counter. This advancement allows for more meaningful and clear versioning, where each branch reflects its own release reality, rather than the overall CI activity. The branch-scoped sequence IDs are implemented as a first-class capability in Harness CI, allowing tags and version strings to be more intuitive and aligned with actual branch activity. This development fills a significant gap in the CI tooling landscape, offering a more streamlined and coherent approach to build numbering without requiring additional scripts or plugins.

Integrating comprehensive testing methodologies into continuous delivery pipelines is crucial for enhancing software quality and system resilience, with platforms like Harness enabling the orchestration of both traditional and emerging testing strategies such as Chaos Engineering. The shift from traditional development and quality assurance silos to more integrated roles, like the software development engineer in test (SDET), has allowed for better collaboration and quality assurance in software development. Testing methodologies often fall into two categories: testing what you made, such as feature testing, and testing the impact, like integration and system testing. Code coverage, unit tests, and open-source dependency tests are emphasized in ensuring the quality of software, while integration, soak, and performance testing validate system behavior under load. Chaos Engineering introduces controlled failures to build system resilience, and test-driven development (TDD) ensures that features align with requirements by focusing on test cases before development. The incorporation of robust pipelines that automate and orchestrate testing processes is essential, with platforms like Harness offering tools to manage these pipelines and improve software delivery outcomes.

CI/CD migration is a complex process that involves transitioning the systems that validate pull requests, produce artifacts, and promote releases to production. To ensure a smooth migration, it is recommended to approach it like a developer platform launch by defining "no disruption" metrics, preparing foundational elements such as runners and networking, and rolling out changes in waves with parallel runs. The migration encompasses various layers including workflow definitions, execution, integrations, and governance, which must be moved or re-implemented carefully. Key strategies such as the use of parallel run, strangler pattern, or big bang depend on the team's risk tolerance and system architecture. Execution layers should be designed to maintain fast feedback and reliability, while identity, secrets, and governance require attention to enhance security. The migration should be piloted for parity, reliability, and developer experience before expanding in waves. Harness CI can aid in reducing migration friction by providing standardized pipeline patterns and improving build performance. After migration, focus shifts to optimization and preventing drift, with continuous improvement based on pre- and post-migration metrics.

Harness AI revolutionized support for a major financial institution by deflecting 95% of platform support tickets through its integration into the software delivery life cycle. As the institution's delivery velocity increased, the complexity of its software development life cycle also grew, leading to a spike in support tickets that the platform team had to manage reactively. Rather than expanding headcount, the organization integrated Harness AI into its operations, using it as a first line of inquiry for developers, who were encouraged to seek answers from the AI rather than the support team. Powered by a Knowledge Graph that maps the software delivery environment, Harness AI provides specific, contextually relevant answers, helping engineers troubleshoot issues independently and efficiently. This shift allowed the platform team to refocus on forward-looking architectural designs, transforming the support model from reactive to proactive and enabling innovation without the friction of overwhelming support requests.

Leon Adato, a Principal Technical Marketing Engineer at Cribl and host of the Technically Religious podcast, was featured on the ShipTalk podcast at SREday NYC 2026, where he shared insights about navigating failure and uncertainty in complex systems through lessons drawn from unexpected sources like the movie Spider-Man: Into the Spider-Verse. Adato emphasized that failures, whether due to obsolete tools or vendor issues, should be viewed as opportunities for teams to rethink their systems and improve architecture, rather than as personal failures. He advocates for transparency and honesty in addressing glitches, which leads to healthier engineering cultures and stronger teams. By comparing the challenges faced by SREs to those encountered by Miles Morales in the film, Adato highlights the importance of staying calm, refocusing, and trusting one's experience during outages, reinforcing that the true measure of success lies in how engineers respond to inevitable technological changes and failures.

Argo CD is a Kubernetes-native continuous delivery controller that adheres to GitOps principles, ensuring that Git serves as the source of truth and continuously reconciles the cluster's running state with the declared state in Git. This guide outlines the installation of Argo CD using a Helm-first approach, emphasizing security, high availability, and multi-team usage, particularly in enterprise settings where it becomes a shared platform infrastructure. Key considerations include using Single Sign-On (SSO), least-privilege Role-Based Access Control (RBAC), and AppProjects for team guardrails. It also details the importance of monitoring, backups, and a staged upgrade process to maintain GitOps reliability at scale, and discusses Helm as the preferred method for enterprise installations due to its ability to pin versions and manage configuration in a repeatable manner. The guide recommends starting with a single shared Argo CD instance and adding more only when necessary, with a focus on maintaining operational maturity through monitoring, alerts, and strategic backups and restorations, thus ensuring that Argo CD remains a reliable shared dependency across various environments and teams.

Radar, a new feature of Harness Bot & Abuse Protection, offers an innovative approach to addressing sophisticated bot attacks by visualizing the intricate connections and behaviors that characterize these threats. This intelligent threat visualization engine transforms abstract attack data into an interactive map, enabling security teams to quickly identify hidden relationships and shared threat signatures, thus accelerating the response time from hours to mere seconds. Radar provides explainable security by making detection logic transparent, allowing security operations centers (SOC) to understand and justify why certain clusters of users are flagged. It is designed to meet the needs of high-stakes environments, such as financial institutions and e-commerce, by handling complex fraud investigations with ease. The tool also integrates AI-powered insights for contextual understanding and offers robust export capabilities for seamless integration with existing tools, enhancing the efficiency and depth of threat response efforts.

Continuous Integration and Continuous Delivery (CI/CD) best practices are essential for accelerating software delivery without compromising quality or stability. This involves implementing strategies such as trunk-based development, frequent small commits, and using intelligent test selection to ensure quick feedback and reduce pipeline time. To maintain stability, it's crucial to prioritize fixing any failed builds immediately, ensuring that the main branch remains deployable. Adopting artifact immutability and using standardized pipeline templates can prevent inconsistencies and streamline the deployment process. Security measures should be integrated from the outset, including automated checks and limited access to CI/CD systems, while progressive delivery techniques like canary deployments can mitigate risks associated with new releases. Monitoring and measuring pipeline performance with metrics such as DORA can drive continuous improvement, making CI/CD a team effort that aligns with a DevOps culture. The Harness Software Delivery Platform is highlighted as a tool that integrates these best practices, enhancing efficiency through features like intelligent caching, role-based access control, and seamless integration with collaboration tools.

Flaky tests, which are automated tests that yield inconsistent results without code changes, consume significant developer time and cost engineering companies millions due to lost productivity. These tests arise from issues like timing problems, test pollution, unstable infrastructure, and race conditions. AI-powered detection and modern CI platforms, such as Harness CI, can automatically identify, quarantine, and isolate flaky tests, thereby maintaining signal quality without manual intervention. This approach not only restores developer trust in test results but also enhances productivity by reducing time spent on false investigations and reruns. Addressing flaky tests involves systematic detection, quarantine, and remediation, alongside implementing best practices to prevent new flaky tests from emerging. By doing so, teams can achieve faster feedback loops, lower infrastructure costs, and cultural improvements in testing practices.

A DevOps pipeline is a comprehensive, automated workflow that streamlines software delivery by integrating code building, testing, deployment, and monitoring into a seamless process, significantly reducing manual work and improving reliability. By automating key stages like Continuous Integration (CI) and Continuous Delivery (CD), these pipelines facilitate faster software releases, enhance code quality, and foster stronger collaboration across development teams. The approach emphasizes early testing, security checks, and frequent feedback, enabling organizations to adapt quickly to market demands and reduce risk through consistent and repeatable processes. Platforms like Harness unify various elements of CI/CD, release management, and continuous verification to support scalable and secure software delivery across diverse environments, including cloud platforms. Despite their benefits, teams may encounter challenges such as managing numerous tools, integrating legacy systems, and maintaining security, which require clear guidelines and standardized practices to overcome.

Code coverage is an essential metric that measures the percentage of source code executed during automated tests, providing evidence of test completeness and software reliability. It plays a crucial role in continuous integration (CI) pipelines by identifying untested areas, enforcing quality standards through CI gates, and enhancing maintainability. While the goal is not to achieve 100% coverage, strategic use of modern techniques like Test-Driven Development (TDD), AI-assisted test generation, and gamification can increase effective coverage without hindering development speed. Different types of coverage, such as line, function, branch, and mutation coverage, offer insights into various testing aspects, and integrating coverage with CI tools transforms it into a quality gate, ensuring alignment with organizational standards and policies. Platforms like Harness CI facilitate this integration by providing intelligent test selection, analytics, and policy-driven gates, making coverage a first-class CI signal that supports disciplined, fast delivery.

During the ShipTalk podcast episode at SREday NYC 2026, Birol Yildiz, Co-founder and CEO of ilert, discussed with host Dewan Ahmed the transformative role of artificial intelligence in reliability engineering, particularly in incident response. The conversation highlighted a shift from AI tools providing post-incident assistance to actively resolving incidents in real-time by leveraging observability data, deployment changes, and code intelligence. This evolution aims to reduce the operational burden on site reliability engineers (SREs) by enabling AI agents to diagnose and remediate outages autonomously, thus minimizing the need for human intervention during incidents. Birol envisions a future where the role of SREs transitions from responding to alerts to architecting systems that improve developer productivity and minimize customer impact, with AI handling routine incident resolution. This paradigm shift is expected to alleviate the stress of on-call duties and allow engineers to focus on higher-value tasks such as system architecture and innovation in reliability practices, ultimately leading to fewer disruptions and a more efficient incident management process.

Internal platform adoption hinges on developers naturally opting for self-service workflows due to their efficiency and reliability, rather than merely tracking portal usage. Successful adoption requires focusing on a problematic workflow like environment provisioning or onboarding, implementing it end-to-end with benchmarks, and ensuring it is faster and safer than existing methods. Adoption thrives when the platform provides consistent, value-driven experiences with clear ownership, robust CI/CD pipelines, and standardized practices such as Infrastructure as Code. It's crucial for the platform team to be committed and for the initial user experience to be seamless and time-saving. By demonstrating tangible benefits and incorporating developer feedback, platforms can become the default path, reducing reliance on ticket-based systems and encouraging self-service. Harness's Internal Developer Portal exemplifies this approach by integrating orchestration, governance, and workflow automation, ensuring that adoption feels like a win for developers, not an obligation.

Intelligent caching in CI/CD is a strategic approach to enhancing build efficiency by managing Docker layers, dependencies, and artifacts near CI runners, thereby reducing latency, costs, and registry load. This advanced form of caching provides observability, TTL controls, and automated invalidation, differing from basic caching by incorporating policies and metrics for governance. It serves as a pivotal element in optimizing CI/CD pipelines, complemented by test intelligence that selectively runs relevant tests and parallelization that ensures efficient use of resources. Harness CI exemplifies this integration, offering tools like Cache Intelligence and Test Intelligence™ to streamline these processes, enhancing build speed and reducing developer wait time. By implementing these measures, platform teams can significantly improve build times, cut costs, and reduce the volume of "CI is slow" complaints, thereby turning intelligent caching into a strategic advantage.

Parallel execution in Continuous Integration (CI) pipelines can significantly enhance developer productivity and reduce infrastructure costs by allowing independent tasks to run concurrently, thus cutting down on idle time and bottlenecks. This method leverages tools like Harness CI, which offers AI-powered optimizations and automated governance to ensure safe, scalable, and efficient parallelism. Effective parallel execution requires careful planning, such as mapping dependencies, isolating flaky tests, and employing selective testing and intelligent caching to minimize redundant work and control costs. Successful implementation also involves using templates and policy-driven governance to maintain control over parallel processes across teams, which can lead to up to a 76% reduction in infrastructure costs and a fourfold increase in build speed. Legacy systems like Jenkins can transition to modern parallel execution patterns by incrementally applying best practices and automation tools to ensure a smoother migration. Overall, the combination of parallel execution, intelligent optimization, and strategic governance can transform CI pipelines into faster, more cost-effective, and reliable components of the software delivery process.

The redesigned Harness MCP server v2 offers an efficient and scalable MCP-compatible interface for AI agents, reducing tool count from over 130 to just 11 while minimizing context consumption from 26% to 1.6% in a 200K-token window. By adopting a registry-based dispatch model, it supports 125+ resource types without expanding the tool vocabulary, allowing the LLM to focus on reasoning rather than serving as a routing layer. This architecture optimizes agent performance by eliminating excessive context overhead, enabling more effective execution of complex workflows within developer environments. The v2 server also includes built-in safety controls such as confirmation for writes, fail-closed deletes, and a read-only mode, ensuring secure and reliable operations. Through the integration of Harness Skills, the server supports guided workflows that enhance usability and efficiency, making it a robust solution for deploying AI-driven DevOps processes across platforms like Cursor and Claude Code while maintaining compatibility with various MCP clients.

At the SREday NYC 2026, the ShipTalk podcast featured a conversation with Zachary Gruenberg from Palo Alto Networks about the challenge of machine identity management in modern infrastructure. As AI agents increasingly automate tasks like incident response and infrastructure management, the need for secure management of machine identities has become crucial, since these non-human entities require credentials to access systems. Zachary and podcast host Dewan Ahmed discussed how the proliferation of machine identities can lead to security risks, especially when identity management is not prioritized in automation deployments. They emphasized the importance of integrating identity management into the security architecture to manage credentials, apply the principle of least privilege, and automate identity lifecycle processes. The conversation highlighted the common oversight of losing track of machine identities, which can lead to "identity sprawl" and security vulnerabilities, particularly as AI agents take on more complex roles. As organizations adopt more AI-driven automation, maintaining strong identity security practices becomes essential to ensure that automation systems can function safely while protecting critical infrastructure.

AI-powered pipeline optimization significantly enhances CI/CD processes by accelerating build times up to four times and reducing infrastructure costs by up to 76%. This optimization approach employs techniques such as smart test selection, intelligent caching, and ephemeral build environments to minimize unnecessary work, providing developers with immediate feedback and reducing the feedback cycle by 40%. By utilizing standardized templates with automated policy enforcement and built-in analytics, platform teams can enhance security and governance without hindering developer efficiency. The strategy emphasizes balancing governance and developer autonomy, employing composable templates with policy guardrails, temporary build machines, and cross-cloud security measures to maintain operational control and optimize multi-cloud environments. These methods not only streamline processes but also introduce predictive optimization and self-healing capabilities, which foresee and rectify issues proactively, thus transforming CI/CD systems into more efficient and cost-effective solutions.

End-to-end (E2E) testing presents significant challenges due to the need to simulate real user interactions across dynamic environments, and traditional test automation methods often lead to unreliable and costly maintenance cycles. AI-driven testing offers a solution by shifting from script-based automation to prompt engineering, where the quality of natural language instructions directly impacts test stability and reliability. Effective prompts in AI testing encompass clear goals, context, specifics, assertions, and boundaries, allowing for self-healing tests that adapt to UI and data changes, thereby reducing maintenance efforts. Harness AI Test Automation exemplifies this approach by using an agentic AI testing architecture that interprets intent-driven prompts to autonomously execute and validate tests, emphasizing the importance of precise and self-contained instructions. This method enables teams to create robust, CI/CD-ready tests, promoting faster software delivery with minimal maintenance while maintaining high quality.

Resilience testing is a critical, continuous practice in the Software Development Life Cycle (SDLC) for distributed systems, as outages are inevitable even with the most reliable cloud providers. Uma Mukkara, Head of Resilience Testing at Harness, emphasized that resilience should not be viewed as a "day-two problem" but rather integrated into the SDLC alongside functional and performance testing to continuously verify system behavior across system failures, load conditions, and disasters. Delaying resilience validation leads to "resilience debt," which accumulates risks and increases business impact when failures occur. A holistic approach to resilience testing, combining chaos engineering, load testing, and disaster recovery testing, alongside cross-team collaboration and AI-driven insights, enhances system reliability. Harness has developed a unified platform for resilience testing, providing a comprehensive view to identify and address resilience risks, ensuring that resilience becomes a core discipline of modern software delivery.

CI/CD tools are essential software platforms that automate the processes of code integration, testing, release preparation, and deployment, thereby enhancing software delivery speed and reliability while maintaining governance and security. These tools facilitate a seamless transition from code commitment to production, helping teams deliver smaller, frequent updates with reduced manual intervention and risk. Modern CI/CD platforms, like Harness, incorporate AI and deep insights, offering intelligent automation that minimizes scripting and enables efficient governance, allowing developers to focus more on coding. Continuous Integration (CI) involves frequent code merging with automated builds and tests, while Continuous Delivery (CD) and Continuous Deployment extend automation to release processes, with CD often requiring manual approval, and Continuous Deployment executing fully automated releases. Security is integral to CI/CD pipelines, ensuring vulnerabilities are addressed early, with features like secure code analysis and access control. The choice of CI/CD tools should consider factors like automation depth, integration capabilities, scalability, and security features to align with team needs and compliance requirements.

AI-generated code faces challenges in software delivery due to a lack of context, not model quality, highlighting the importance of a cohesive system that reflects the interrelationships between various components like pipelines, environments, and policies. Knowledge graphs are proposed as a solution to transform fragmented DevSecOps data into operational truth, emphasizing that overmodeling, undermodeling, and stale context can hinder their effectiveness. AI-assisted DevOps is seen as a preliminary stage where AI helps with tasks like code writing and log summarization, while AI-operational DevOps aims for AI to understand and manage the entire software delivery process. The text argues for the necessity of a shared context layer to allow AI to operate effectively, suggesting that operational reasoning, rather than mere automation, is the goal. The critical role of freshness and relevance in maintaining effective knowledge graphs is stressed, advocating for a focus on minimal, use-case-driven modeling that addresses real-time needs.

Harness has introduced two new products, AI Security and Secure AI Coding, to enhance its DevSecOps platform in the AI era, addressing the challenges posed by AI's impact on software development and security. AI Security focuses on protecting AI components within applications by integrating API security, while AI Discovery, AI Testing, and AI Firewall are new features that identify and protect against AI-specific threats. Secure AI Coding aims to address vulnerabilities in AI-generated code by integrating security checks directly into the code generation process, leveraging tools like Cursor and Claude Code to detect issues as code is written. These innovations address the growing concerns over AI-native applications' vulnerabilities and the need for a comprehensive security approach that connects the entire software delivery lifecycle, ensuring both AI applications and AI-generated code are secure from inception to production.

AGENTS.md is an emerging standard for improving AI agent collaboration within code repositories by providing a structured, tool-agnostic format for project-specific instructions. Unlike README.md files intended for human developers, AGENTS.md is designed to deliver precise, operational guidance to AI agents, detailing build commands, test execution steps, and other necessary instructions to ensure consistent and predictable agent behavior. This format addresses the issue of environmental ambiguity that AI agents face when navigating repositories designed for humans, by offering a centralized and version-controlled source of truth that reduces tool lock-in, preserves institutional knowledge, and minimizes context decay and inconsistent outputs. Adoption of AGENTS.md across various industries and open-source projects signifies a shift towards engineered context and a more stable contract between humans and machines, which is becoming increasingly crucial as AI agents integrate into daily development workflows. In a broader sense, AGENTS.md represents an essential foundational step toward optimizing AI agent functionality within software engineering environments, ensuring clarity and consistency before implementing advanced orchestration and optimization techniques.

AI is rapidly expanding in enterprise environments, often outpacing the ability of security teams to manage it effectively, as organizations struggle with limited visibility into AI systems and the data they process. The article emphasizes the urgent need for AI Security Posture Management (AI-SPM) due to the rapid, uncontrolled integration of AI technologies, a systemic lack of visibility, and the potential exposure of sensitive data. It highlights a framework for AI security, which involves discovering AI assets, understanding sensitive data flows, assessing risks, and operationalizing these insights into existing security operations. The piece discusses how tools like Harness can aid in continuously discovering and classifying AI assets, mapping sensitive data flows, detecting vulnerabilities, and integrating AI security signals into workflows like Jira and SIEM, thereby helping organizations govern AI security continuously and reduce regulatory risks.

APIs have transformed data interaction for developers, yet they pose security challenges, necessitating robust protection to prevent hacking and failures. API failures occur when responses deviate from expected behavior, often due to issues like incorrect permissions, unsecured endpoints, invalid session management, expiring APIs, bad URLs, overly complex endpoints, and exposed IPs. Common error codes such as 400, 401, 403, 404, and 500 reflect issues ranging from malformed requests to server errors. The text highlights methods to mitigate these failures, including securing API keys, managing permissions, using token authentication, caching data, and monitoring IP addresses. Harness WAAP by Traceable offers solutions like identifying and mitigating API risks, leveraging distributed tracing, and applying machine learning for comprehensive API security across development cycles.

As AI accelerates software development, organizations are encountering significant challenges in the delivery phase, a phenomenon termed the "AI Velocity Paradox." Research indicates that while AI tools enhance code generation speed, they also expose weaknesses in current software delivery systems, leading to frequent deployment issues and increased manual workload for developers. This is primarily because many delivery pipelines, developed incrementally over time, rely heavily on manual tasks, which are not scaling well with the faster pace of AI-driven development. To address these challenges, it is crucial for organizations to modernize their delivery systems by standardizing processes, automating quality and security checks, incorporating guardrails, and fostering a feedback loop to better manage and mitigate risks. By treating software delivery as an integrated system rather than a collection of tools, companies can transform increased development speed into safer and more effective delivery of software solutions.

Harness Database DevOps integrates Open Policy Agent (OPA) to enforce database governance through policy as code, enabling organizations to meet compliance standards without hindering development speed. By embedding compliance rules directly into CI/CD pipelines, teams can automatically prevent risky database changes, maintain audit trails, and adhere to regulations such as GDPR, HIPAA, and PCI-DSS. This approach addresses common challenges in database compliance, such as complex regulatory requirements, lack of visibility, and risks associated with manual processes. Harness employs a policy-driven model where OPA policies are written in the Rego language, allowing for precise governance over database changes, access, and configurations. The integration of OPA into database DevOps practices ensures that compliance controls are consistent, auditable, and automatically evaluated before changes reach production, transforming governance from a manual task into an automated, integrated part of the workflow. By doing so, organizations can maintain developer productivity while ensuring that compliance and security standards are met, ultimately protecting sensitive data and minimizing risks.

A centralized software catalog revolutionizes the management of microservices by unifying service metadata, ownership, and governance, transforming fragmented environments into a cohesive, actionable platform. By incorporating AI-powered automation, reusable templates, and integrated GitOps, it enables teams to efficiently scale deployments while minimizing manual effort and tool sprawl. The catalog not only acts as a service inventory but also serves as a central hub, linking services to their owners, environments, deployment pipelines, and compliance rules. This integration facilitates rapid pipeline creation, compliance management, and incident response by providing developers with self-service capabilities and a clear view of service dependencies and governance. With built-in policy-as-code and immutable audit trails, software catalogs ensure strong governance without hindering developer velocity, allowing platform teams to maintain compliance and security while accelerating delivery.

Engineering teams increasingly rely on frameworks like DORA metrics and the SPACE framework to measure developer productivity and its impact on business outcomes, moving beyond traditional vanity metrics that focus merely on activity. Effective productivity measurement requires a shift from tracking operational motion to demonstrating strategic outcomes, emphasizing the importance of context over raw numbers. This involves integrating technical execution with business impact, ensuring metrics are aligned with customer value, product velocity, and strategic goals. High-performing teams use a multi-dimensional approach, combining DORA's delivery health metrics with SPACE's focus on team satisfaction and alignment, to create a comprehensive view of engineering effectiveness. Harness SEI is highlighted as a platform that provides context-rich insights by connecting various stages of the development lifecycle, enabling organizations to move from justifying engineering costs to demonstrating value creation. This strategic intelligence approach transforms productivity measurement from a mere reporting task into a tool for informed decision-making and investment alignment.

Harness Artifact Registry is a cloud-native platform designed to revolutionize artifact storage and management by addressing common challenges associated with traditional Docker registries. As a fully OCI-compliant system, it not only supports Docker containers and various container formats but also integrates seamlessly with CI/CD pipelines, policy engines, and vulnerability scanners. The platform emphasizes security by design, incorporating features like container vulnerability scanners, dependency firewalls, and policy sets to proactively manage and secure artifacts throughout their lifecycle. Users can push, store, and manage Docker images and other artifacts with ease, benefiting from automated security checks and policy enforcement. Additionally, its integration with Harness CI/CD pipelines streamlines the build and deployment process, offering complete visibility and governance while minimizing the risks associated with credential leaks and supply chain vulnerabilities. This comprehensive approach ensures that software delivery pipelines are both secure and efficient, aligning with modern compliance requirements and reducing exposure to potential threats.

The State of AI-Native Application Security 2025 research report highlights a growing divide between developers and security professionals, exacerbated by the rapid integration of AI applications within organizations, which are increasingly posing security risks. With 62% of respondents unable to identify where large language models (LLMs) are located within their organizations, and 75% acknowledging the evolution of AI applications outpacing security measures, the report underscores significant vulnerabilities. Despite the potential for collaboration, only a minority of developers consult security teams before initiating or launching AI projects, and a substantial 74% of security leaders perceive developers as viewing security as a hindrance to innovation. However, the report suggests that by adopting AI-native security resilience, organizations can simultaneously harness AI's value and ensure security, advocating for a cultural shift towards shared governance, real-time monitoring, AI-specific threat testing, and consistent security measures throughout the application lifecycle. The integration of platforms like Harness and Traceable is recommended to unify development and security efforts, ensuring seamless collaboration and reducing the burden on developers by automating security processes and enhancing AI visibility.

Matthew Skelton, CEO & CTO of Conflux, shares insights ahead of the DevOps Modernization Summit, emphasizing the strategic use of AI, the limitations of DORA metrics, and the necessity of integrating governance and compliance into platforms. He critiques the current trend of adopting AI without clear objectives, arguing that it should be used to enhance team effectiveness and reduce non-value-adding tasks rather than generating unnecessary code. Skelton also warns against the misuse of DORA metrics, suggesting that focusing on fundamental capabilities rather than outputs is more beneficial for software quality. He highlights the rapid pace of technological and regulatory changes, advocating for automated compliance that is trusted by teams, as manual inspections are no longer sufficient. Overall, Skelton underscores the importance of starting with desired outcomes, selecting context-appropriate metrics, and building trust in automated systems to achieve engineering excellence.

Modern database design has shifted from being a one-time event to an ongoing process that evolves with changing business needs and system behaviors. Initially, database schemas were seen as immutable constructs designed early in a project, but this mindset has evolved to accommodate continuous and incremental changes, such as adding columns or indexes, to maintain backward compatibility and adapt to new requirements without disrupting production. The integration of database schema evolution into continuous integration and delivery (CI/CD) pipelines through Database DevOps practices, like version control and automated migrations, ensures reliable database changes and enables organizations to manage complexity and operational risks effectively. As databases scale and performance pressures mount, evolutionary design emphasizes extending existing schemas over complete redesigns, allowing for gradual adaptations that reflect deeper operational insights. Platforms like Harness Database DevOps facilitate this process by providing visibility, traceability, and governance, ensuring that database changes are predictable and safe, and positioning the database as a dynamic record of learning that aligns with the realities of modern software delivery.

The text explores the challenges and potential benefits of integrating AI into engineering systems, emphasizing that while AI can accelerate code production, it often creates bottlenecks in delivery due to insufficiently adapted processes, pipelines, and governance frameworks. It highlights the common mistake of measuring AI's success solely by increased code output rather than actual system outcomes that deliver customer value efficiently and safely. The document outlines a three-layer AI ROI measurement model that includes utilization, impact, and cost, urging organizations to focus on system-wide improvements rather than individual productivity gains. Moreover, the importance of establishing robust governance and standardized pipelines is stressed to ensure that AI-enhanced development leads to faster, safer, and cost-effective delivery. The text also provides insights into measuring AI ROI through metrics that reflect delivery speed, quality, cost efficiency, and system resilience, advocating for a shift from vanity metrics to those that truly indicate business value.

Animesh Pathak, a Developer Relations Engineer at Harness, is dedicated to advancing Database DevOps, APIs, testing, and open-source innovation, playing a crucial role in developing scalable workflows that enhance secure and reliable software delivery. With a B.Tech in Computer Science and extensive experience in software engineering, AI, cloud computing, and Kubernetes, he holds multiple certifications and actively contributes to open-source communities like Alphasians, GSoC, and CNCF. Pathak is passionate about mentoring and fostering communication, technical expertise, and best practices in an inclusive environment. His diverse expertise is reflected in a wide range of topics covered in his work, including database deployments, AI in test automation, cloud cost optimization, and DevSecOps, showcasing his commitment to bridging the gap between developers and data teams.