April 2026 Summaries

Harness has introduced the Harness Cursor Plugin, which integrates its AI-native software delivery platform directly into Cursor, aiming to streamline the entire software delivery process from code changes to production without leaving the editor. This integration addresses the "AI Velocity Paradox," where the rapid pace of AI-assisted coding is hindered by slower, fragmented delivery processes. The plugin allows developers to use natural language commands within Cursor to manage CI/CD pipelines, security checks, approvals, and deployments, thereby maintaining governance and operational insight without manual orchestration or tool-switching. Harness's approach leverages a Software Delivery Knowledge Graph to provide context for AI-assisted actions, ensuring that software delivery is not just faster but also governed and auditable. By connecting the coding and delivery processes seamlessly, the plugin represents a shift towards a more integrated and efficient workflow, aligning AI advancements with robust delivery controls.

In the evolving landscape of enterprise AI, the focus is shifting from selecting the right model to developing a robust "agent harness" that governs how AI models operate within organizations. An agent harness is a critical framework that controls what an AI agent remembers, the context it accesses, the tools it can utilize, and the actions it can perform, ensuring secure and compliant operations. This concept is particularly crucial in software engineering, where AI agents are being utilized to autonomously write, edit, and deploy code, requiring a dual-loop system to manage both software development and delivery. The inner loop focuses on individual productivity in coding, while the outer loop addresses broader organizational execution and risk management. Without a proper harness, AI agents pose security and compliance risks, acting as new attack surfaces and potentially causing significant organizational disruptions. Therefore, a software delivery agent needs a comprehensive framework consisting of memory, context, tools, and verification to safely and effectively manage software delivery. The foundation for such a system already exists in platforms like Harness, which integrates these elements to ensure that AI-driven processes are both efficient and secure, ultimately enabling a seamless integration of AI agents into existing software delivery pipelines.

Organizations often face complex challenges when managing Infrastructure as Code (IaC) across multiple tools like Terraform, OpenTofu, and Terragrunt due to a lack of unified orchestration strategies. Incremental adoption of these tools without a cohesive management approach leads to fragmented workflows, inconsistent policy enforcement, and governance challenges. The main issue isn't the tools themselves but their disparate state management and workflow expectations, which create orchestration problems that masquerade as tooling problems. Effective IaC management requires an orchestration layer that treats execution engines as deployment details rather than architectural boundaries, ensuring consistent policy enforcement, state visibility, and audit trails across all workspaces. Solutions like Harness IaCM offer a unified orchestration layer that integrates seamlessly with multiple IaC tools, enabling consistent policy evaluation, drift detection, and workflow management, thereby enhancing governance and operational efficiency without forcing standardization on a single tool.

The text is a compilation of various articles and insights by Shibam Dhar, focusing on cloud computing, DevOps, and CI/CD practices, particularly through the lens of tools like Kubernetes, Harness, and Spinnaker. It covers a wide range of topics, including cost management in the cloud, feature flags, GitOps, and the benefits of continuous integration and delivery. The collection emphasizes improving developer productivity, optimizing cloud resources, and enhancing software delivery efficiency. It also provides tutorials and best practices for using tools such as GitHub Actions, Snyk, and AWS EKS-Anywhere, aiming to guide developers in modern software development and delivery techniques.

Harness IaCM has announced the integration of native Terragrunt support, enhancing enterprise-grade orchestration by allowing teams to manage Terraform, OpenTofu, and Terragrunt within a single platform. This development aims to eliminate the fragmented tooling often encountered in complex, multi-environment deployments by providing built-in governance, policy enforcement, and approvals to streamline secure infrastructure operations. The addition of Terragrunt support signifies a significant advancement toward a unified, multi-IaC control plane, enabling greater visibility, drift detection, and resource-level infrastructure change management. As Infrastructure as Code becomes standard in cloud operations, Harness IaCM addresses the growing complexity by unifying multi-tool strategies, thus supporting a consistent experience and shared policies across various frameworks. The platform is evolving to incorporate expanded IaC support and AI-driven automation to further simplify infrastructure management tasks, ultimately aiming to enhance flexibility, governance, and automation for modern teams operating at scale.

The release of Anthropic Mythos and Project Glasswing signifies a transformative phase in software development, emphasizing the need for integrating security and engineering as automated models expedite vulnerability exploitation. This evolution presents an opportunity to create more robust systems through AI-accelerated defense, urging engineering teams to take a proactive role in reshaping modern software architecture. Success in this new era requires dismantling traditional silos between security and engineering, establishing shared accountability, and crafting joint roadmaps with clear responsibilities. Core engineering imperatives include accelerating patch cycles, integrating secure coding practices, and building systems with resilience and simplicity. Additionally, organizations should plan for unexpected challenges by understanding software dependencies and developing technical work-arounds. Security teams must evolve by leveraging AI to streamline operations and automate incident response, thus enhancing the speed of resolution. Engineering leaders are encouraged to lead these changes to ensure the long-term viability and strength of their products.

Harness Database DevOps has integrated Percona Toolkit for MySQL to enable safer and more efficient schema changes, utilizing pt-online-schema-change to minimize the risks associated with native MySQL ALTER TABLE operations. This integration allows modifications to be made during business hours by creating a shadow table with the new schema and copying data over in small chunks, reducing lock time and maintaining synchronization with triggers before an atomic swap. Users can activate this feature by selecting Liquibase as the schema configuration in Harness DB DevOps and checking the option to use Percona Toolkit. However, caution is advised as certain DDL operations, like adding or dropping foreign keys, may not be suitable for this process, and proper database user privileges are necessary. This advancement aims to eliminate the need for scheduling schema changes during off-hours and encourages testing in non-critical environments before full deployment. Jesse Wang, the Product Marketing Manager for Harness Database DevOps, emphasizes the potential for zero downtime database schema migrations with this tool.

Mean Time to Failure (MTTF) is a crucial metric in assessing the reliability of non-repairable components, such as Kubernetes pods and CI/CD runners, by measuring the average operational time before failure. It is distinguished from Mean Time to Repair (MTTR) and Mean Time Between Failures (MTBF), which focus on repairable systems and uptime between failures, respectively. MTTF serves as a decision-making tool rather than a mere dashboard statistic, aiding platform teams in planning capacity, setting realistic Service Level Objectives (SLOs), and reducing developer workload by identifying and prioritizing components that frequently fail. The text underscores the importance of using MTTF to forecast incidents, prioritize components based on operational cost, and enhance business outcomes by integrating it with SLOs, error budgets, and AI-powered automation to improve reliability and reduce toil. Practical ways to improve MTTF include stabilizing CI pipelines, employing progressive delivery and rollback strategies, enforcing pipeline governance, and validating resilience through chaos engineering.

Application security has evolved to encompass various stages of the software development lifecycle, with vulnerabilities found in code, open-source packages, container images, and cloud infrastructure. The challenge lies not just in identifying these vulnerabilities but in prioritizing and fixing those that pose significant risks, which requires correlating multiple data points to understand the context and exposure. The shift in responsibility for application security from dedicated AppSec teams to DevSecOps and platform engineering teams reflects the need for integration between development and cloud security workflows. Tools like Harness and Wiz facilitate this integration by providing runtime context and code-level insights, helping teams prioritize vulnerabilities based on real-world exposure. This collaboration allows both DevSecOps and cloud security teams to work within their existing frameworks while sharing critical signals, reducing noise, and ensuring a comprehensive view of security risks across the SDLC.

Modern DevOps practices often overlook databases, causing challenges in maintaining consistency and visibility across environments. While many development teams utilize GitOps principles for application code, database changes, especially with MongoDB, are often managed outside this workflow, leading to potential inconsistencies and risks during deployment. MongoDB stores data in a flexible, JSON-like format called BSON, which is efficient but requires disciplined management of schema changes and indexing. To mitigate these issues, it's recommended to integrate database changes into the same Git-driven workflow used for application code. By treating database updates like application code, changes are versioned, reviewed, and validated within pipelines, ensuring consistent and traceable deployments across environments. This GitOps approach involves defining changes in Git, using pull requests for reviews, and automating deployments through pipelines, which helps maintain structure, reduces human error, and enhances auditability. Harness DB DevOps offers tools to facilitate this process, shifting the role of database teams from manual oversight to designing automated guardrails, allowing for faster, more predictable, and scalable database management alongside application delivery.

AI-Powered Custom Authentication Generation is a new feature designed to streamline the setup of authentication configurations in dynamic application security testing, addressing a common bottleneck in the process. Traditional methods of configuring authentication, such as manual scripting and trial-and-error adjustments, can be time-consuming and error-prone, often requiring significant engineering support. The new AI-driven approach allows users to generate and refine authentication configurations using natural language and large language models (LLMs), significantly reducing setup time from hours to seconds. This functionality supports various authentication types, including API keys, JWTs, and bearer tokens, and allows for iterative refinement without manual edits, enhancing transparency and control with inline visibility and change tracking. By simplifying authentication setup, this feature improves scan success rates, reduces dependency on manual interventions, and facilitates quicker onboarding of new applications, ultimately enabling more efficient and scalable security workflows.

Harness has redefined its Legal team by integrating AI into its operations, transforming traditional legal tasks and improving efficiency. The team not only utilizes AI tools for contract reviews, legal research, knowledge management, data discovery, and privacy compliance but also develops these tools themselves to better serve their needs. This AI-driven approach allows for faster turnaround times, higher quality outcomes, and more meaningful collaboration with other business units, as routine tasks are automated and complex issues receive focused attention. The cultural emphasis on collective learning and sharing ensures that AI implementation is both safe and continuously evolving, with every AI output being reviewed and validated by qualified attorneys before use. This innovative use of AI in the legal domain reflects Harness's broader strategy of embedding AI across all teams to enhance productivity and work satisfaction.

At Harness, the Legal team has embraced an AI-first approach, transforming traditional legal operations by developing and utilizing AI tools to streamline processes and enhance efficiency. This innovative strategy enables the team to perform tasks such as contract review, legal research, and knowledge management more effectively, while also ensuring compliance with data privacy and regulatory requirements. By building AI agents and leveraging AI-assisted platforms, the team reduces turnaround times and enhances collaboration with other departments, shifting its role from a mere checkpoint to a strategic partner within the company. The adoption of AI fosters a culture of continuous learning and improvement, with regular knowledge-sharing sessions that ensure the team remains at the forefront of legal technology. This approach not only improves the quality and speed of legal services but also aligns with Harness’s company-wide integration of AI into everyday operations, offering a model for how AI can be effectively woven into business practices.

The Harness MCP (Model Context Protocol) server is designed to enhance agent infrastructure by minimizing context overhead and focusing on a small set of composable, self-describing abstractions. Inspired by systems like Unix, Git, and SQL, it emphasizes small, stable interfaces, uniform contracts, and runtime introspection. The server acts as a kernel, mediating access to underlying systems and enabling agents to operate efficiently by focusing on reasoning rather than parsing numerous endpoints. With a design that includes generic tools and a dispatch mechanism, the MCP server supports scalable capability growth while keeping the agent's context focused on essential reasoning tasks. By applying principles such as demand-paged schema discovery and centralized dispatch, the server optimizes for runtime selection and composition, which is crucial for the next generation of agent-native systems.

In the rapidly evolving technological landscape of 2026, disaster recovery testing is crucial for ensuring business resilience against potential disruptions such as cyber-attacks, power outages, and software glitches. This ongoing process, which includes various testing methods like tabletop exercises, simulations, operational walkthroughs, and both partial and full failovers, helps organizations validate their recovery plans and maintain system integrity. The integration of artificial intelligence in disaster recovery testing has transformed it into a continuous, automated process, allowing for early detection of potential issues and providing data-driven insights for improvement. Modern tools, like Harness's Resilience Testing module, streamline the process by combining chaos testing, load testing, and recovery testing into a unified platform, supporting seamless integration into daily operations. Regular testing not only reduces downtime and financial losses but also ensures compliance with regulatory requirements, making it indispensable for businesses aiming to safeguard their operations and maintain customer trust in an increasingly interconnected world.

Setting up Terraform securely and consistently is crucial for scaling infrastructure and maintaining compliance, emphasizing the importance of isolating Terraform runs, encrypting state files, and using lock files to ensure integrity. The text stresses automating Terraform management within CI/CD and GitOps pipelines for consistent versions and rapid delivery across services, advocating for Infrastructure as Code Management tools to maintain governance and scalability. It warns that insecure or inconsistent Terraform setups can hinder delivery and underscores the need for secure installations using verified binaries, encryption, and automated CI/CD integration. Best practices include using vendor-backed packages, pinning exact versions, automating post-install checks, and utilizing centralized platforms for execution to enhance security and auditability while pushing for enterprise-level automation and governance to harness the full potential of Terraform in infrastructure management.

Cloud migrations, often perceived as straightforward, involve complex transformations, including shifts from monolith to microservices, API rewrites, and on-prem to cloud infrastructure moves. Traditional "big bang" release strategies, where changes are launched simultaneously, centralize risk and lack adaptability, often resulting in failure to achieve intended outcomes. An effective alternative is the adoption of progressive delivery strategies using feature flags, which decouple deployment from release, allowing for gradual, measured exposure and minimizing risk. This approach, exemplified by the strangler fig pattern, enables controlled traffic routing and real-time performance measurement, thus transforming migrations into a series of validated steps rather than high-risk events. The success of such strategies hinges on robust measurement of key metrics and the ability to revert changes instantly if issues arise, facilitating a data-driven, observable, and reversible migration process.

Integrating A/B testing and feature flags into CI/CD pipelines enables developers to conduct self-service experimentation while ensuring enterprise governance and security. This approach streamlines experimentation workflows, reduces operational bottlenecks, and addresses technical debt within large engineering teams. By leveraging AI-powered automation, platform teams can scale safe experimentation and gain portfolio-level visibility and ROI measurement without compromising control or compliance. The practice of testing in production, which involves validating new features in live environments, complements pre-production testing by providing real-world validation, enhancing speed and efficiency, and improving user experience through rapid feedback loops. Feature flags facilitate safe testing by allowing incremental feature releases, and when combined with A/B testing, they support data-driven decision-making processes. The integration of these practices into CI/CD pipelines not only enhances software delivery stability but also optimizes user experiences, enabling precise monitoring and control over feature rollouts, ultimately transforming every deployment into a controlled experiment rather than a gamble.

Terragrunt is an open-source tool developed by Gruntwork that enhances Terraform by addressing common issues such as code duplication, environment consistency, and dependency management in complex, multi-environment infrastructures. By standardizing remote state management and enabling DRY (Don't Repeat Yourself) configuration through shared inheritance, Terragrunt simplifies the management of Terraform configurations across multiple AWS accounts or other cloud environments, making it especially beneficial for large organizations operating in regulated industries that require strict governance and audit trails. Terragrunt acts as an orchestrator, running Terraform in dependency order and maintaining consistency across environments, which is crucial for teams managing numerous microservices and environments. However, it can introduce unnecessary complexity for smaller teams or simpler setups, and it requires a learning curve for new users due to its additional configuration layer and conventions. While Terragrunt does not replace continuous delivery platforms, it complements them by providing infrastructure provisioning capabilities within broader CI/CD workflows, making it a valuable tool for enterprises seeking to scale their infrastructure management efficiently.

Modern engineering teams have achieved remarkable speed in software deployment through continuous integration and continuous delivery (CI/CD) platforms, but this acceleration often overlooks the crucial aspect of ensuring system reliability and customer satisfaction. A notable incident at a global bank illustrated this gap when a seemingly successful deployment of a digital payment platform inadvertently degraded performance, highlighting the need for continuous verification. This process involves integrating real-time observability data into deployment pipelines to ensure that new software versions meet expected performance and business outcomes. By comparing real-time system behavior against established baselines, continuous verification can automatically halt and roll back deployments if performance issues arise, thereby preventing customer impact and maintaining operational excellence. The adoption of continuous verification not only enhances technical reliability but also fosters a cultural shift where engineers can deploy frequently without fear of undetected regressions, ultimately transforming observability tools from post-deployment diagnostic aids into proactive components of the deployment decision-making process.

Harness Engineering is adapting to the evolving landscape of AI integration in software development by redefining their engineering processes to achieve excellence. This involves embracing Spec-Driven Development, where specifications are meticulously authored and stored in a shared repository for consistent feature development, and leveraging agent-driven workflows to create dynamic user experiences. The focus on engineering excellence includes rigorous testing strategies across APIs, agents, and products, with operational readiness reviews (ORR) to preemptively address potential issues during development. Operational reviews are conducted regularly to ensure product resilience and service availability, drawing from best practices at leading tech companies like AWS and Google. As Harness transitions into an era dominated by AI-powered systems, their engineering strategies—ranging from proactive operational excellence to agent-ready architecture—are designed to maintain speed and quality while scaling to meet the demands of increasingly complex customer needs.

Feature testing is a modern approach in DevOps that enables teams to deploy code changes safely and incrementally by using feature flags and progressive delivery, thus minimizing risks associated with traditional big-bang releases. This method allows for features to be initially deployed in an inactive state and gradually exposed to users through controlled rollouts, ensuring real-time verification and impact measurement before full deployment. The Harness Feature Management & Experimentation platform integrates enterprise-scale feature flags with AI-driven release monitoring and automated rollbacks to streamline the process, reducing manual oversight and potential risks. By treating each feature as a testable component, this strategy improves the safety and reliability of CI/CD pipelines, aligning with principles of Site Reliability Engineering and allowing for continuous improvement without accruing technical debt.

A diverse group of women in the software industry has been recognized for their contributions to solving complex problems, mentoring the next generation, and enhancing the quality of software development. These honorees, drawn from various roles such as engineering directors, product owners, and delivery managers, are acknowledged by their peers, teammates, customers, and partners for their impactful work in areas like cloud and platform engineering, DevOps, and developer experience. The celebration highlights their leadership and influence within the software community, underscoring the importance of recognizing and learning from female leaders in technology.

Unified experimentation platforms that integrate feature flags, progressive delivery, and real-time analytics enhance the ability of CTOs to measure innovation effectively and safely. These platforms, equipped with AI-powered guardrails and automated issue detection, significantly reduce operational risks by allowing teams to identify and fix issues early without compromising governance. By deeply integrating with CI/CD pipelines and observability tools, developers can experiment daily with confidence that each release is tied to data and real business outcomes. Modern platforms like Harness Feature Management & Experimentation stand out by integrating seamlessly into existing development workflows, thus minimizing friction that could hinder adoption and ensuring that feature deployments are both data-driven and strategically targeted. Progressive delivery methods, feature flag management, and AI-driven experimentation streamline the process, allowing for faster iterations and more immediate impact analysis. Emphasizing psychological safety and data-driven releases, these platforms not only accelerate delivery pipelines but also align technical innovations with board-level metrics, ensuring that every experiment contributes measurably to the organization's strategic objectives.

Cloud cost visibility becomes increasingly challenging as organizations scale their multi-cloud environments, primarily due to the structural inadequacies of traditional tracking systems that cannot keep pace with the exponential growth in resource types, deployment patterns, and organizational complexity. Multi-cloud fragmentation creates information silos where providers such as AWS, Azure, and GCP use different billing models that do not natively communicate, necessitating custom ETL pipelines for data consolidation, which often lag behind actual spending. The FinOps community has developed the FinOps Open Cost and Usage Specification (FOCUS) to address this issue by providing a common schema for cloud billing data, with support from major cloud providers but uneven adoption among SaaS vendors. Tagging strategies, crucial for cost allocation visibility, frequently fail due to inconsistent application by developers and automated systems, while cost data often lacks the real-time granularity necessary for operational decision-making. These challenges lead to issues such as loss of accountability, reactive optimization efforts, and ineffective budget alerts, suggesting a need for automated, real-time anomaly detection and unified multi-cloud cost tracking that integrates closely with engineering workflows to support sustainable cost governance.

The blog post by Prerana Singhal highlights the journeys and insights of women in the tech industry, emphasizing the shared themes of curiosity, community, and confidence that have driven their careers. Through personal stories from women at Harness, including their defining moments and challenges, the narrative underscores the importance of breaking stereotypes and building technical depth regardless of gender. The significance of community and mentorship in fostering growth is a recurring theme, with calls for more practical support like hands-on workshops and structured mentorship programs. The culture at Harness is praised for its focus on merit-based growth and inclusion, which empowers women to make meaningful impacts in technology. Looking to the future, the post expresses optimism about the increasing accessibility of technology and encourages women to take initiative, build confidence through action, and contribute their voices to the tech landscape.

Site Reliability Engineering (SRE) integrates engineering principles into operations to enhance system reliability through automation, measurable targets, and efficient incident management, embodying a shift from traditional manual processes. Originating at Google, SRE codifies operational tasks via concepts like Service Level Indicators (SLIs), Service Level Objectives (SLOs), and error budgets, which help balance deployment speed with system stability. AI-powered Continuous Delivery (CD) and GitOps platforms automate verification and rollbacks, reducing manual toil and accelerating incident recovery, crucial in microservices architectures where failures can cascade. SRE practices involve progressive delivery strategies, such as canary releases, automated rollbacks, and policy-as-code guardrails, ensuring safe, rapid delivery while maintaining service availability. The discipline addresses deployment anxiety and incident response with structured roles, blameless postmortems, and observability focused on user-impacting symptoms. SRE and DevOps complement each other, with SRE providing strict engineering frameworks to operationalize DevOps principles, enhancing reliability through disciplined automation and proactive engineering rather than reactive firefighting.

The text discusses the challenges and solutions involved in effectively using agents to answer complex queries across multiple data types and sources. It highlights the traditional difficulties of relying on raw API calls, which often lead to cumbersome and error-prone processes, and contrasts this with the advantages of structured data infrastructure. This infrastructure includes a domain ontology, a relationship graph, and a query engine, which collectively enhance agent capabilities by providing pre-validated, structured data that reduces errors and improves query performance. The text argues that by investing in robust data infrastructure, organizations can significantly enhance agent quality across dimensions such as correctness, groundedness, safety, trajectory, and performance. The dispatch table model is proposed as a more efficient approach than endpoint-per-tool designs, enabling agents to navigate complex queries with fewer errors and more efficiency by using a limited set of generic verbs. Ultimately, the text suggests that a well-modeled data infrastructure not only supports better agent performance but also allows for continuous improvement and scalability without requiring changes to the agent's reasoning layer.

Harness Database DevOps integrates governance directly into the deployment pipeline, ensuring consistent, automated, and proactive management of database changes. By embedding governance within the delivery process, it evaluates changes against predefined policies using Open Policy Agent (OPA) before execution, considering factors like environment and database type. This approach allows context-aware policy enforcement, such as blocking destructive operations in production while offering flexibility in development environments. Governance is treated as code, enabling versioning and consistent application across teams and environments, streamlining audits through a comprehensive record of database activity and reducing manual oversight. Harness also provides visibility across environments to prevent drift and improve delivery outcomes, offering combined metrics across application and database changes. This integration facilitates better governance, compliance, and delivery speed, as demonstrated by customers like Athena Health, who experienced enhanced change orchestration and a clearer understanding of database states.

Disaster Recovery (DR) testing has evolved into a critical engineering discipline, essential for maintaining service continuity and customer trust during cloud outages, as demonstrated by the AWS Middle East incident. Despite the importance of DR testing, many organizations neglect it, failing to simulate realistic failure scenarios and validate recovery objectives such as Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO). The unprecedented drone strikes on AWS data centers in the Middle East on March 1, 2026, highlighted the vulnerability of even the most reliable cloud infrastructures to geopolitical risks, underscoring the necessity for comprehensive DR plans that account for a wide spectrum of failure scenarios. Harness Resilience Testing offers a solution by enabling organizations to simulate AWS region failures safely and repeatedly, transforming DR testing from an annual compliance exercise into a continuous, integrated practice. This approach allows companies to validate their recovery strategies in a structured, repeatable manner, preparing them to effectively handle regional cloud disasters.

The Harness Web Application & API Protection (WAAP) MCP Server addresses the challenges faced by security teams due to overwhelming data and complex interfaces by seamlessly integrating security data with AI tools. This new solution provides a standardized interface through the Model Context Protocol (MCP), enabling easier access and analysis of security insights without complex setups. It facilitates real-time threat inspection, controlled data access, and integration with AI-powered workflows, helping teams make faster, informed decisions. The server's design allows for the management of permissions and governance, ensuring compliance while enabling dynamic, context-driven queries, particularly suitable for large language models (LLMs) and AI agents. This integration aims to overcome the limitations of traditional security platforms, which often present unintuitive access and lack integration standards, by providing a consistent and reliable means to access and leverage security data in AI ecosystems. The Harness WAAP MCP Server is available at no additional cost to current subscribers, promoting efficient security management and strategic decision-making.

As chatbots become increasingly prevalent across various applications, the challenge of testing these systems effectively at scale emerges due to their non-deterministic nature. Unlike traditional software systems where expected outputs for given inputs are predictable, chatbots generate varied, semantically equivalent responses, rendering conventional test automation frameworks inadequate. This necessitates the use of AI-driven test automation, such as Harness AI Test Automation (AIT), which evaluates chatbot outputs based on semantic understanding rather than syntactical validation. AIT allows testers to specify criteria for appropriate responses in natural language, shifting focus from exact matches to assessing whether the chatbot meets the defined criteria. Practical tests demonstrated that AI Assertions could effectively evaluate chatbots on hallucination, mathematical reasoning, prompt injection resistance, harmful content refusal, factual accuracy, adherence to tone and instructions, multi-turn consistency, and logical reasoning, thereby addressing critical quality, safety, and reliability concerns in conversational AI systems.

AI's effectiveness in engineering is contingent on its access to connected and comprehensive contextual data, which is often hindered by fragmented systems within organizations. While AI tools are increasingly integrated into engineering workflows to perform tasks such as incident investigation and automation, their potential is limited by siloed data environments where crucial information like service catalogs, deployment data, and incident histories are dispersed across unconnected systems. This fragmentation restricts AI's ability to provide accurate and meaningful insights. The future of AI in engineering depends significantly on the design of connected platforms that unify services, teams, workflows, and operational signals into a cohesive context layer. Platform engineering, traditionally focused on enhancing developer productivity, now plays a strategic role in structuring environments that facilitate effective AI deployment by ensuring all aspects of the engineering lifecycle are interconnected and dynamically updated. This shift necessitates a reevaluation of internal developer portals, transforming them from static directories into integral parts of a living knowledge ecosystem that AI can leverage to deliver more reliable and comprehensive outcomes.

Self-service workflows in platform engineering, while initially removing the friction of environment creation, often fail to address long-term challenges such as ownership, lifecycle management, cost, and security, leading to environment drift and operational bottlenecks. While these workflows reduce the initial ticket volume and enable faster environment setup, they do not inherently manage the environments over time, resulting in a return of tickets concerning maintenance, updates, and security issues. The article highlights the importance of treating environments as managed systems rather than one-time outputs and emphasizes that managing environments involves ongoing responsibilities related to ownership and governance that self-service actions typically overlook. This gap between self-service and effective environment management can lead to increased costs and security risks, with platform teams often reverting to traditional ticket-based processes to address lifecycle issues. The text argues for a shift in perspective, viewing environment management as a continuous system rather than a series of isolated tasks, and suggests that successful long-term self-service strategies require clear ownership, lifecycle governance, and guardrails to prevent drift and maintain operational clarity.

Self-service infrastructure transforms traditional ticket-driven processes into automated, efficient workflows, enabling developers to access necessary resources safely and on demand while maintaining security and compliance through policy-as-code and standardized templates. The Internal Developer Portal (IDP) acts as a central hub, allowing developers to request environments and resources without needing deep cloud expertise, with workflows executed via Infrastructure as Code and Continuous Delivery pipelines that incorporate built-in governance. By defining rules and guardrails upfront, self-service infrastructure ensures consistent and auditable governance, reducing delays and risks associated with manual approvals. Harness operationalizes this model by integrating an IDP with Infrastructure as Code Management and Continuous Delivery, enabling fast, consistent, and compliant deployments. Starting with a focused "golden path" and gradually expanding, organizations can demonstrate ROI in 90 days by measuring adoption, speed, and policy outcomes. The use of AI-powered automation further enhances the self-service model by streamlining onboarding, policy creation, and deployment verification, ultimately enabling scalable and frictionless operations across environments.

Developer self-service is a platform engineering approach that enables developers to independently manage resources through a curated internal developer portal, reducing reliance on centralized teams and alleviating ticket bottlenecks. When implemented effectively with opinionated golden paths, automated guardrails, and integrated real-time metrics, it transforms platform engineering from a reactive ticketing operation into a proactive, measurable product. This model promotes faster delivery cycles, improved reliability, and enhanced security, as developers can choose from standardized workflows, receive instant feedback, and access comprehensive documentation within a single platform. By focusing on high-demand, repeatable tasks for self-service and maintaining rigorous policy as code guardrails, organizations can ensure compliance and security while empowering developers. A structured 90-day rollout plan can demonstrate the ROI of developer self-service without disrupting existing workflows, while continuous measurement through metrics and scorecards proves its effectiveness in improving outcomes and reducing operational toil.

Harness is transforming software delivery environments by automating and integrating them into its platform, bridging the gap between Continuous Delivery (CD) and Infrastructure as Code (IaC). This approach eliminates manual, ticket-driven processes, enabling teams to create consistent, self-service environments that are fully governed and automated, thus reducing operational overhead and accelerating innovation. Harness's Environment Management system employs Environment Blueprints to standardize and simplify the creation of production-like environments, ensuring governance and compliance without sacrificing speed or control. By incorporating role-based access control and drift detection within its platform, Harness provides enterprises with a unified control plane for provisioning, deployment, and lifecycle management, addressing the hidden bottleneck in platform engineering and allowing developers to focus on innovation while maintaining necessary oversight and governance.

At SREday NYC 2026, the ShipTalk podcast featured a conversation with Phil Christianson, Chief Product Officer at Xurrent, exploring the balance between product strategy, engineering investment, and platform reliability. Christianson discussed the challenge product leaders face in prioritizing innovation against the need for a stable and reliable platform, especially with the increasing pressure to quickly deliver new features, driven by emerging technologies like AI. He emphasized that focusing solely on new features can lead to technical debt, while an exclusive focus on stability can leave organizations trailing behind competitors. The key, according to Christianson, is to ensure that innovation and reliability are not in competition but evolve together, with strategic investments in areas such as observability and infrastructure automation. He also highlighted the importance of empowering engineering teams to take ownership of their systems, integrating reliability practices directly into development workflows, and viewing reliability engineering as a catalyst for innovation rather than a barrier. This approach, he argued, is crucial for building scalable platforms that can adapt effectively and maintain customer trust.

Harness employs a schema-driven Knowledge Graph to enhance the performance and reliability of AI agents operating across its multi-module platform, which includes CI/CD, DevSecOps, and FinOps. This approach offers a more efficient alternative to the Model Context Protocol (MCP) that relies on raw API calls, which can lead to high token costs, latency, and errors. By utilizing the Harness Query Language (HQL) to access structured platform data, the Knowledge Graph ensures deterministic and low-latency answers, reducing token usage by up to 25 times compared to MCP. The Knowledge Graph stores comprehensive metadata for fields, explicitly declares cross-module relationships, and uses semantic annotations to efficiently route queries, thus minimizing the need for the AI to infer connections or interpret data incorrectly. This structured approach not only enhances the AI's ability to retrieve and analyze data but also supports a tiered data ownership model that prioritizes using the Knowledge Graph for maximum reliability and cost-effectiveness.

AI prototypes often face significant challenges when transitioning from impressive demos to reliable, large-scale production systems, as highlighted by Shubham Jindal, Director of AI at Harness. The complexities lie not in the AI models themselves but in the surrounding infrastructure, which requires careful attention to context, evaluation, memory, and governance. The creation of a knowledge graph as an organizational memory layer is emphasized as a solution to provide coherent context, while tool protocols like MCP help integrate various systems. Successful AI deployment involves consolidating into unified agents for efficiency and accuracy, rigorous evaluation processes to catch subtle errors, personalized and contextual memory for user interactions, and robust governance to ensure compliance and safety. Jindal advises starting small with specific, well-defined use cases and emphasizes building the foundational infrastructure before optimizing AI models, suggesting that the true value lies in crafting systems that support reliable and scalable AI applications.

Innovation in software development is progressing rapidly, yet software delivery remains a bottleneck, often burdening Platform and DevOps engineers with manual tasks. Harness introduces AI for GitOps to alleviate these challenges by offering an intelligence layer that enables management, monitoring, and troubleshooting of GitOps environments using natural language. This AI-driven solution enhances standard GitOps tools by providing fleet-wide troubleshooting, agentic configuration management, and intelligent workflow orchestration, thus reducing the manual toil associated with managing multiple instances and out-of-sync applications. By leveraging Harness's AI capabilities, enterprises can improve their Mean Time to Recovery (MTTR) and ensure robust governance, thereby facilitating faster and safer software delivery without increasing headcount. The AI integrates seamlessly with existing systems, adhering to enterprise-grade governance with policies like RBAC and OPA, ensuring every action is auditable.

Ansible and Terraform are complementary tools used in infrastructure automation, with Terraform excelling in provisioning cloud resources through a declarative approach and state management, while Ansible is proficient in configuration and orchestration tasks using idempotent modules. Combining these tools within managed GitOps workflows enhances enterprise-level automation by reducing manual steps, preventing configuration drift, and ensuring compliance. Harness Continuous Delivery offers an AI-powered control panel to manage both tools, providing unified visibility, governance, and policy enforcement for complex deployment pipelines. This integration is beneficial for large organizations as it centralizes visibility and governance, streamlines workflows, and maintains compliance without hindering developer productivity. The strategic use of Terraform for infrastructure setup and Ansible for system configuration allows for efficient, scalable infrastructure management, particularly useful in environments involving multiple cloud providers and extensive services.

In 2026, disaster recovery testing has become a critical, evolving discipline due to increasing threats such as cyber attacks and cloud outages. Regular testing is essential to minimize downtime, ensure systems can be swiftly restored, and maintain business continuity. Modern tools incorporating automation and artificial intelligence have simplified and enhanced the testing process, enabling continuous validation of recovery plans, uncovering weaknesses, and providing actionable insights for improvement. These tools, like the Resilience Testing module from Harness, integrate various testing methods including chaos, load, and disaster recovery testing, offering a streamlined approach that fits into daily operations. With AI, testing becomes more accurate and predictive, allowing businesses to identify and address potential issues before they escalate. As reliance on digital infrastructure grows, regular disaster recovery testing not only protects revenue and customer trust but also helps meet regulatory requirements, making it an indispensable practice for ensuring resilience in an increasingly connected world.

Authentication and authorization are crucial components of modern web applications and APIs, serving distinct yet complementary roles in security. Authentication (authN) verifies identity by confirming whether a user, service, or machine is who they claim to be, often using multi-factor authentication methods like passwords, hardware tokens, or biometrics. Authorization (authZ), on the other hand, determines what authenticated identities can access or perform, utilizing models such as Role-Based Access Control (RBAC) or Attribute-Based Access Control (ABAC) to enforce permissions. Modern applications often authenticate users once but authorize them at every sensitive access point, ensuring a layered defense against unauthorized actions. This distinction is vital to avoid systems where mere authentication could mistakenly grant users excessive privileges. Tools like Harness Web Application & API Protection (WAAP) can provide runtime protection, enhancing security by integrating robust authentication and authorization with features like API discovery and real-time threat defense. It's essential to design clear authentication and authorization models early in development to prevent security issues and ensure compliance with regulatory requirements.

Chaos engineering, often misunderstood as a reckless approach reserved for tech giants, is actually a disciplined and accessible practice that can be beneficial for organizations of any size. Matt Schillerstrom, drawing from his experience at Target.com, debunks myths by explaining that chaos engineering involves hypothesis-driven, controlled experiments designed to build trust and confidence in systems. Contrary to the belief that it is only for large companies like Netflix and Google, smaller-scale tests can reveal valuable insights into system resilience without requiring massive infrastructure. Furthermore, chaos engineering complements rather than replaces traditional testing methods, enhancing the realism and value of existing testing workflows by incorporating failure scenarios. Ultimately, the goal is not to create chaos but to foster a deeper understanding of system behavior and to build confidence in handling unexpected challenges.

Harness is advancing security in continuous integration and continuous delivery (CI/CD) by introducing a Zero Trust model to supplement traditional Role-Based Access Control (RBAC) and Open Policy Agent (OPA) methods, which are often insufficient against insider threats or compromised accounts. This model involves the Harness Delegate, a runner in the delivery infrastructure, which pauses execution to send task details to a Zero Trust Validator controlled by a security team, ensuring actions are verified at execution time rather than at initial access. This approach aims to enhance security by blocking potentially malicious actions before they can execute, acting as a vital "Last Line of Defense" against threats while allowing engineers to maintain high-speed innovation. By integrating this model, Harness aims to address the vulnerabilities highlighted by incidents like the Checkmarx GitHub Action attack, reinforcing its commitment to a hardened architecture that supports enterprises' security demands in software delivery.

The text discusses the impact of AI on software development, highlighting how AI coding assistants have accelerated code writing and committing but shifted bottlenecks to later stages such as builds, security, and deployment pipelines. It introduces various features and tools integrated into Harness, aimed at improving efficiency and security across the software development lifecycle, including AI skills for IDEs, GitOps troubleshooting, OPA policy enforcement, and vulnerability prioritization using EPSS. The text also covers enhancements in cloud cost management, incident response, and database DevOps, emphasizing the importance of AI in streamlining these processes. The author, Chinmay Gaikwad, brings attention to the comprehensive updates released by Harness, showcasing how AI-driven solutions can enhance engineering velocity when applied across the entire delivery chain.

Modern CI/CD platforms have accelerated the speed of software delivery, allowing for quick and frequent deployments. However, in regulated environments like the financial sector, speed must be balanced with governance, auditability, and control. A financial institution addressed this by redesigning their delivery architecture to separate the authoring and execution of pipelines. Non-production environments were used for development and testing, while production environments executed only pre-approved templates, ensuring governance and compliance. This model integrated strict policies, such as using Git as the source of truth for template versioning and requiring pull requests for changes, which improved auditability and control. By embedding governance directly into the pipeline architecture, the institution maintained a secure and compliant delivery system, demonstrating an effective approach to platform engineering that balances speed with security and governance.

Agentic coding is an advanced form of AI-assisted development where autonomous agents perform multi-step coding tasks more efficiently, akin to a fast-working junior engineer. Unlike traditional AI code assistants that respond to individual prompts, agentic coding allows AI to pursue a goal end-to-end, making follow-up decisions independently. This shift requires a structured environment provided by Internal Developer Portals (IDPs), which offer crucial elements like trusted metadata, standardized workflows, and executable policies, ensuring agents can operate safely and effectively. Harness's Internal Developer Portal facilitates the adoption of agentic coding by centralizing service metadata, enforcing policy-as-code, and supporting platform flows, thereby addressing potential risks such as scaled errors and configuration drift. By creating a governed control plane, IDPs help maintain productivity and accountability as agents become integrated into software delivery workflows.

The shift from Source Code Management (SCM) to Source Context Management is driven by the integration of AI agents throughout the software development lifecycle, necessitating a deeper, semantically rich understanding of codebases. As AI agents become integral to planning, coding, reviewing, and deployment, the need for a context engine that offers pre-computed, queryable, and semantically complete representations of code becomes essential. This context engine is structured in a three-layer architecture comprising semantic indexing, a code knowledge graph, and agentic integration, enabling AI agents to access and act on comprehensive codebase context without directly reading files. This transformation promises increased developer productivity, faster delivery velocity, and reduced risk by ensuring that agents operate with a deeper understanding similar to that of experienced human engineers. The article emphasizes the importance of robust security measures, polyglot repository support, and incremental updates in the implementation of Source Context Management, highlighting its potential as a new infrastructure primitive for modern software development.

The report explores the impact of AI coding tools on developer teams, revealing a complex dynamic between speed and stability in software development processes. While AI-assisted coding has accelerated deployment frequencies, with many teams deploying products daily or multiple times a week, it has also introduced challenges such as increased deployment problems, security vulnerabilities, and compliance issues. These issues are more pronounced among users who frequently rely on AI tools, suggesting that while AI can enhance productivity, it may simultaneously heighten risks and workload, contributing to developer burnout. The study underscores the need for improved automation and standardized processes to manage the increased pace and complexity effectively. Despite the challenges, many teams rate their DevOps capabilities highly, indicating a recognition of the need for further improvement in managing AI's integration into development cycles. The report is based on a survey conducted by Harness and Coleman Parkes, involving 700 engineering practitioners and their managers across several countries.

Harness Database DevOps integrates production data testing into CI/CD pipelines, enabling teams to effectively validate database schema changes, performance, and rollback paths with real-world data. This structured approach enhances collaboration and delivery confidence by transforming database deployments into a repeatable, evidence-driven practice, reducing production risks. The process involves defining a pipeline with stages like environment provisioning, schema application, automated rollback testing, and comparison against a baseline to ensure correctness before reaching production. Harness Database DevOps bridges the gap between development, operations, and database teams by providing visibility, governance, and standardization, allowing database changes to be managed with the same rigor as application code. This comprehensive framework not only removes uncertainty from the delivery cycle but also aligns database changes with application code flows, producing auditable proof of correctness and elevating database change management to a disciplined engineering practice.

The text explores the problem of "context rot" in AI systems, where the performance of AI models degrades as they handle larger context windows, leading to issues such as inconsistent logic and hallucinations. It emphasizes that context rot is a structural limitation, not a bug, and explains how models distribute attention across tokens, causing important instructions to lose weight as context grows. The text suggests that effective context management, including structured reasoning, stepwise execution, and session resetting, is critical to maintaining AI system performance. It highlights the importance of meta-prompting and checkpoints to prevent context degradation and ensure reliable outcomes. The article connects this discussion to a previous part that addressed the need for a standardized instruction layer, like AGENTS.md, to make repositories agent-native, and indicates that the next part will explore building systems with multiple agents and external integrations while preserving context and consistency.

Bri Strozewski discusses the complexities and evolving nature of software supply chain security, emphasizing that while Software Composition Analysis (SCA) provides visibility into open-source vulnerabilities, it is insufficient on its own to protect against the broader array of threats facing modern applications. The discussion highlights that CI/CD pipelines, considered privileged infrastructure, are vulnerable to attacks as they hold critical credentials and deployment paths, making them targets for compromise. Artifact integrity is another critical concern, as malicious actors can tamper with artifacts at various stages, including after they have been built. The text also warns of the risks posed by container ecosystems and third-party integrations, which can introduce vulnerabilities if not properly managed. Furthermore, the integration of AI components adds new dimensions to supply chain security, requiring organizations to consider AI provenance, data lineage, and runtime behavior monitoring. The article advocates for a comprehensive approach to supply chain security, extending beyond SCA to include structured controls throughout the software delivery lifecycle, ensuring governance and security are embedded at every stage from code development to AI deployment.

Cost awareness in CI/CD workflows is crucial for preventing unexpected financial burdens and ensuring efficient resource management without hindering development speed. The integration of automated cost feedback loops, pipeline budget guardrails, and tools like Harness Cloud Cost Management can help align DevOps velocity with financial accountability by providing real-time cost visibility and enabling informed decision-making before deployment. Traditional CI/CD pipelines often lack inherent cost governance, leading to a disconnect between engineering and finance teams, but embedding cost tracking at the pipeline level allows for better attribution of expenses to specific deployments, fostering more collaborative and informed discussions. By shifting cost ownership closer to engineers and making cost a first-class operational metric, organizations can improve decision-making and maintain a balance between innovation and financial control. This approach helps teams optimize for performance and functionality while keeping budgets in check, ultimately enhancing the efficiency and sustainability of cloud spend management.

Shift-Left FinOps is a proactive approach to cloud cost optimization that integrates financial governance directly into the development lifecycle, embedding cost controls early to prevent waste before infrastructure is deployed. By utilizing Policy as Code and Infrastructure as Code (IaC), along with automated guardrails, organizations can enforce cost governance, thus allowing developers to make informed, cost-aware decisions in real-time. This method contrasts with traditional FinOps, which typically involves retrospective financial reviews of cloud expenditures after deployment, often leading to reactive and inefficient cost management. Implementing Shift-Left FinOps involves three key components: automating cost visibility and feedback loops, integrating cost control into development workflows, and codifying cloud cost governance through Policy as Code. This approach enhances cloud financial management, provides real-time cost insights to developers, and ensures consistent governance across multiple cloud environments, ultimately supporting sustainable cloud growth and more efficient operations.

Harness Database DevOps provides a robust solution for database governance by integrating Open Policy Agent (OPA) to enable policy-driven compliance within development workflows, addressing challenges such as complex regulations, lack of visibility, and human error. By embedding OPA policies directly into database pipelines, Harness automates the enforcement of compliance controls, turning what was once a manual process into an integral part of the DevOps lifecycle. This approach ensures consistent, auditable, and automated evaluation of compliance rules before any database changes are deployed, thereby reducing risks and enabling organizations to meet regulatory standards like GDPR, HIPAA, and PCI-DSS more efficiently. By adopting this model, organizations can maintain a balance between speed and control, ultimately transforming database governance into a scalable and integrated practice that supports secure and agile software delivery.