January 2026 Summaries

Harness AI is enhancing its intelligent automation capabilities as it enters 2026 by introducing three new AI-powered features aimed at improving post-code processes, including incidents, security, and test setups. The updates focus on accelerating workflows, maintaining control, and allowing AI to manage tedious tasks. Key advancements include human-aware incident analysis that integrates human insights from conversations into operational data, AI-driven API naming to enhance security signal clarity, and natural-language-generated authentication scripts for faster API security testing. Additionally, the new AppSec Agent enables natural language queries for security data, simplifying the process of obtaining insights and generating policies. These features align with Harness AI's vision of optimizing software delivery by integrating human and machine signals, reducing false positives, and improving governance and compliance through policy-aware AI and auditability.

Harness has been recognized as a Customer Educator in Wiz's WIN Partner Index 2025, highlighting its role in enhancing cloud security within development workflows by integrating security into CI/CD pipelines. This accolade underscores the importance of education and context in modern security ecosystems, as it allows for faster remediation, clearer risk prioritization, and scalable security. Harness's partnership with Wiz reflects a shared commitment to embedding security early in the software development lifecycle, providing teams with the necessary insights to act on security risks effectively. The recognition builds on a history of collaboration between the two companies, with Harness being the first CI/CD platform vendor certified to partner with Wiz. This collaboration offers end-to-end security visibility, enabling customers to correlate risks across APIs, infrastructure, and runtime environments. The WIN Partner Index serves as a benchmark for the most valuable integrations in cloud security, emphasizing the significance of real-world adoption and impact in shaping modern security programs.

HashiCorp's transition of Terraform from the Mozilla Public License v2.0 to the Business Source License has significantly impacted the Infrastructure as Code (IaC) community, highlighting the importance of licensing as a platform risk rather than a mere legal footnote. The change restricts Terraform's use in creating competing commercial services, forcing teams to reconsider their IaC strategies and sparking a fork of the last MPL-licensed version into OpenTofu, which is governed under the Linux Foundation. OpenTofu offers a clear, open-source alternative to Terraform, maintaining compatibility and continuity without legal ambiguity, while Terraform remains a strong option for teams with existing investments. This shift emphasizes the need for infrastructure teams to design resilient platforms that can adapt to future licensing changes, underscoring the operational challenges of balancing openness with commercial needs. Harness Infrastructure as Code Management (IaCM) supports both OpenTofu and Terraform, providing consistent workflows and governance to facilitate gradual transitions and reduce disruption, allowing organizations to navigate these changes effectively.

The text explores the challenges and considerations of managing Infrastructure as Code (IaC) in growing teams, particularly focusing on the decision between building custom IaC solutions and purchasing standardized IaC Management (IaCM) platforms. As teams scale up from managing a few environments to handling hundreds of workspaces, custom IaC solutions often become complex and fragile, leading to issues in governance, security, and consistency. Harness IaCM is introduced as a solution that offers out-of-the-box pipelines and templates for both OpenTofu and Terraform, providing standardized workflows, governance, and security features, thus reducing the operational overhead associated with custom IaC maintenance. The text emphasizes the importance of making a deliberate build vs. buy decision, suggesting that for most teams, adopting an IaCM platform like Harness offers faster implementation, better governance, and enhanced reliability.

Kubernetes, while powerful and efficient, can lead to unexpected cost overruns due to small, often unnoticed scheduling decisions rather than the platform itself. These inefficiencies stem from over-provisioned resource requests, poor bin-packing, wrong node selections, and idle infrastructures, all of which quietly drive up cloud expenses. To mitigate these costs, it's crucial to adopt cost-aware scheduling, right-sizing, and smarter node selection strategies, treating cost as a key metric alongside performance and scalability. Techniques such as using taints and tolerations, pod affinity, strategic bin-packing, and scheduled scale-downs can reduce unnecessary expenses by optimizing resource usage and ensuring workloads are efficiently allocated. Employing tools like Cluster Orchestrator and Harness CCM for autoscaling and scaling down idle resources can further streamline operations and enhance cost-effectiveness. By making cost optimization an intentional and transparent part of the workflow, teams can maintain performance without sacrificing financial prudence.

The concept of the "Argo ceiling" describes a predictable scaling challenge encountered when using Argo CD and GitOps for application deployment on Kubernetes, rather than being a fault of Argo CD itself. As organizations expand their clusters, teams, and environments, the initial success and trust in GitOps lead to fragmentation in visibility, governance, and orchestration without a control plane, resulting in inefficiencies such as fragmented visibility, script-heavy workflows, and secret sprawl. To address these challenges, a GitOps control plane can centralize control without centralizing ownership, orchestrate structured workflows rather than relying on scripts, and automate guardrails for consistency and compliance. Harness offers a solution as a GitOps control plane, providing unified visibility, structured orchestration, AI-assisted deployment verification, and secure secret management to help teams efficiently scale their GitOps operations beyond the Argo ceiling.

The rapid advancement of AI in software development, particularly in code generation, has created a significant challenge known as the AI velocity paradox, where the speed of AI-driven development outpaces the maturity of security, testing, deployment, and compliance processes. This imbalance concerns industry leaders, with almost half of surveyed organizations worried about vulnerabilities and compliance issues from AI-generated code. The paradox is most evident in security, as AI can expand the threat surface by generating new application components or using unverified open-source models. Traditional security measures often struggle with non-deterministic AI agents, making it crucial to prioritize AI security mitigation strategies, such as addressing prompt injection, sensitive data disclosure, and excessive agency. Harness's approach to addressing these challenges includes AI asset discovery, AI security testing, and runtime protection to help organizations manage risks and build resilient AI-native applications. Looking ahead, the evolving attack landscape will require a focus on understanding decision risks and managing the non-deterministic nature of AI applications through enhanced visibility, testing, and runtime protection.

Harness has introduced the Human-Aware Change Agent as part of its AI SRE system, which aims to enhance incident response by integrating human insights with machine intelligence. This tool captures and operationalizes human conversation, treating it as valuable operational data to guide automated investigations during incidents. The AI Scribe listens across an organization's tools, filtering out irrelevant information to capture critical decisions and actions, which are then used to drive change-centric investigations. This approach contrasts with traditional AI-based incident response systems that rely solely on machine signals, as it acknowledges the importance of human-discovered clues in real-world incident management. The system integrates with existing tools like Datadog, PagerDuty, and Jira, facilitating a seamless workflow for teams to resolve incidents faster without increasing headcount. By connecting human insights with system changes, the Human-Aware Change Agent provides evidence-based hypotheses and clear insights, helping teams act quickly and safely, ultimately redefining AI's role in incident management as a collaborative partner rather than a standalone solution.

Harness has been recognized by TechStrong Group with three DevOps Dozen Awards, highlighting its comprehensive, AI-native platform vision that emphasizes efficient, reliable, and quick software delivery. These awards include Best End-to-End DevOps Platform, Best Platform Engineering Solution, and DevOps Industry Leader of the Year for CEO Jyoti Bansal, reflecting the company's modular approach that integrates continuous integration, continuous delivery, and cloud cost management. Harness's Internal Developer Portal (IDP) alleviates developer cognitive load, offering self-service capabilities with automated guardrails, while Bansal's leadership and commitment to AI-driven innovation have propelled the platform forward. The recognition by DevOps.com adds to Harness's momentum and commitment to enhancing its AI agents, automated governance, and module ecosystem, positioning the company as a leader in the evolving landscape of software delivery.

Chaos engineering is a method for validating the resilience of distributed systems by simulating real-world failure scenarios, and it is particularly relevant for infrastructures like Kubernetes, AWS, Azure, and GCP. This approach involves starting with low-impact experiments, such as pod-level faults, and gradually escalating to more significant disruptions like node or zone failures, while always defining clear hypotheses and using probes to measure results. The guide emphasizes the importance of understanding system behaviors under stress, noting that failures such as network issues, availability zone outages, and resource exhaustion are inevitable, and the goal is to ensure systems can handle these gracefully. By implementing structured chaos experiments, teams can gain insights into system vulnerabilities and enhance their production resilience before actual failures occur, thereby building more robust and reliable applications.

Infrastructure guardrails have become essential components in modern cloud operations, particularly with the widespread adoption of Infrastructure as Code (IaC). These guardrails act as preventive controls that help standardize and secure infrastructure deployments, ensuring they align with organizational policies, security best practices, and compliance requirements. As organizations increasingly rely on IaC for cloud-native practices, the complexity and scale of infrastructure management multiply, making guardrails critical to prevent costly mistakes such as security breaches or misconfigurations. By enforcing best practices and incorporating robust security controls, infrastructure guardrails allow teams to innovate safely and efficiently. Tools like Harness Infrastructure as Code Management (IaCM) provide comprehensive support for implementing these guardrails, offering policy enforcement, standardization, controlled deployment workflows, and drift detection. The integration of guardrails ensures that organizations maintain a strong security posture while providing the flexibility needed for rapid deployment and innovation.

Harness AI enhances governance for platform engineering teams by integrating AI-driven policy enforcement into pipeline creation, enabling developers to move quickly while maintaining compliance with organizational standards. Using Open Policy Agent (OPA) policies, Harness AI automates rule enforcement across pipeline, entity, cost, and security scan governance, allowing users to generate Rego code from plain English descriptions and ensuring policies are adhered to in real-time. This approach reduces bottlenecks by scaling governance efficiently and provides full traceability and auditability of AI-generated resources, which are clearly labeled and integrated into the Harness Audit Trail. Harness AI operates within user Role-Based Access Control (RBAC) permissions to prevent unauthorized actions, ensuring security by design. The system facilitates the adoption of pre-approved "golden" pipeline templates, helping developers build compliant pipelines quickly, while offering feedback to address policy violations, thus transforming governance challenges into learning opportunities.

Harness Feature Management & Experimentation (FME) has been investing in OpenFeature, a CNCF standard for feature flagging, since 2022, to address the complexities of modern software development. Feature flags are crucial in allowing teams to ship features safely and test functionality without redeployment, but managing them consistently across multiple services, environments, and languages presents challenges. OpenFeature offers a vendor-agnostic API that standardizes flag behavior across different SDKs and programming languages, aiding in the integration with analytics and CI/CD systems. Harness FME utilizes OpenFeature to simplify flag evaluations, rollouts, and feature impact tracking without altering existing workflows, thus managing the intricate layers of feature management beyond simple boolean toggling. The collaboration aims to meet growing demands for governance, experimentation, and observability in software delivery, and plans to expand support to additional server-side providers like Go and Ruby.