Q1 2026 Product Update: Harness Continuous Delivery & GitOps

| Harness Blog

Welcome back to the quarterly update series! If you've been following along, you've seen how Q3 2025 brought [deeper control and strengthened integrations], while Q4 2025 [closed the year strong] with platform upgrades and quality-of-life improvements. The first quarter of 2026 builds on these foundations with AI-powered continuous verification that eliminates configuration overhead, expanded deployment platform support, and GitOps workflow enhancements that align with how teams actually ship software.

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Deployments

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Azure Container Apps Deployments

Native support for Azure Container Apps brings serverless container orchestration to your Azure workloads with the full Harness deployment experience. Azure Container Apps provides a fully managed platform for running microservices and containerized applications with automatic scaling based on HTTP traffic or events, and now you can deploy to it with the same confidence and control you have for Kubernetes, ECS, and other platforms.

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Harness gives you two deployment strategies designed for Azure Container Apps' architecture. Choose Basic deployments for immediate traffic cutover when you need speed, or leverage Canary deployments with progressive traffic shifting (20% → 70% → 100%) using Azure Container Apps' built-in revision management to validate new versions under real production load. The platform includes an automated rollback that captures container app state before deployment, enabling instant recovery if issues arise. Authentication is flexible—support for both Azure OIDC (keyless authentication) and Service Principal methods means you can deploy across subscriptions using a single connector, with full support for Azure Container Registry (ACR) and Docker Hub as artifact sources.

‍Learn more about Azure Container Apps deployments →

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Accelerating Windows Deployments

This year, we're focusing heavily on Windows deployments to address the performance and scalability challenges that enterprise Windows teams face every day. The two enhancements shipping this quarter are just the beginning—we're bringing the same innovation velocity to Windows deployments that you've come to expect across all Harness platforms. Stay tuned for more Windows Deployment capabilities throughout 2026 that will continue to streamline your deployment processes and eliminate friction in enterprise Windows environments.

‍Learn more about Windows deployments →

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Windows Deployment Session Reuse

Windows Session Reuse eliminates redundant connection overhead by enabling delegate-wide session pooling, cutting connection setup time from 30-60 seconds to instant reuse in JEA environments. When a command step executes, Harness checks the pool for an existing idle session to the target host with matching credentials and reuses it immediately, dramatically reducing pipeline execution time for workflows with multiple command steps.

Learn more about Windows Session Reuse →

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Multi-Host Deployment with Dynamic Targeting

Multi-Host Deployment with Dynamic Targeting extends Windows Deployment credential setup to dynamically target different hosts, enabling true parallel execution across multiple Windows servers. Configure multiple host groups within a single credential configuration, and Harness automatically routes commands to the appropriate servers based on your deployment strategy. This unlocks centralized credential management while maintaining the security boundaries required in JEA environments, enabling teams managing large Windows server fleets to deploy faster with reduced credential sprawl.

Learn more about Multi-Host Windows Deployments →

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Smarter Amazon Elastic Container Service Management

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Amazon ECS deployments get two powerful new capabilities that bring operational flexibility and automation to your container workloads.

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Standalone ECS Scaling

Standalone ECS Scaling lets you scale services up or down without triggering a full deployment, enabling operators to respond to real-time demand without triggering change management processes. The new ECS Scale step lets you modify desired task counts on demand—whether you're responding to traffic spikes, performing maintenance windows, or testing capacity limits—without redeploying your application.

Learn more about ECS scaling →

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ECS Scheduled Actions

ECS Scheduled Actions enable time-based scaling policies directly within your ECS service deployments, eliminating the need to manage scheduled actions separately in the AWS console while keeping your entire ECS configuration under version control. Define scheduled actions to automatically adjust desired task counts at specific times—scale up services before anticipated morning traffic, scale down during off-peak hours, or align capacity with predictable business patterns.

Learn more about ECS scheduled actions →

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Terraform Security Enhancements

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Terraform deployments now include automatic security protections that prevent accidental exposure of sensitive data throughout your pipeline workflows.

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Masking Sensitive Terraform Outputs

Terraform outputs marked as `sensitive = true` are now automatically masked in the Harness UI, preventing accidental exposure of credentials, API keys, and other secrets in pipeline execution logs and output tabs. When Terraform outputs are marked as sensitive, Harness respects that designation and redacts the values wherever they appear—you can still reference these outputs in downstream steps using expressions, but the actual values remain encrypted and hidden from view.

Learn more about masking sensitive outputs →

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Continuous Verification

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This quarter's focus on continuous verification centers on eliminating configuration overhead through AI automation and expanding observability platform integrations. From zero-config deployment health analysis to Git-based configuration management, these capabilities make verification accessible to more teams while reducing the time to production-ready monitoring.

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AI-Powered Continuous Verification

Alongside AI Verify, AI-assisted health source configuration makes traditional verification setup effortless through a guided workflow that discovers available signals from your observability platform, classifies them by deployment impact, and generates verification-ready configurations. Describe your service and monitoring goals in natural language, and the Configuration Agent automatically discovers relevant metrics, organizes them into intelligent categories, and generates the queries and thresholds for you—with human checkpoints for selection and refinement at every stage.

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Fine-tune configurations with simple natural language inputs or create custom composite metrics on the fly. What used to take hours now takes minutes.

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AI Verify eliminates the manual setup complexity that has traditionally slowed the adoption of continuous verification. No more baseline configuration, threshold tuning, or monitored service management. AI Verify deploys lightweight data-collection plugins into your Kubernetes cluster that collect, aggregate, and provide observability data while stripping personally identifiable information before it leaves your environment.

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The plugins gather logs and metrics from your observability platforms and perform statistical and algorithmic anomaly detection. Large language models then contextualize these anomalies against your deployment verification criteria, filter false positives based on business-criticality, and synthesize natural-language root-cause insights with actionable remediation suggestions—all without requiring explicit baseline data. This shifts continuous verification from weeks of configuration work to immediate, intelligent monitoring that understands your services from day one.

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Learn more about AI-powered verification →

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Dynatrace DQL Support for Grail Metrics

Harness Continuous Verification now supports Dynatrace Query Language (DQL) for querying timeseries metrics from Dynatrace Grail, their next-generation data lakehouse. Craft sophisticated metric analysis using aggregation functions, enable dimension-based data splitting for per-instance continuous verification, and combine multiple data sources in a single query. This extends beyond the traditional Full Stack Observability model, giving you direct access to custom metric queries rather than relying solely on predefined metric packs.

Learn more about Dynatrace DQL support →

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GitOps

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GitOps workflows gain AI-powered intelligence, unified notifications, and enhanced PR capabilities this quarter. These improvements streamline application management, improve operational visibility, and align GitOps workflows with how teams naturally collaborate through pull requests.

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AI-Powered GitOps Operations

AI-powered operations management brings natural language queries and intelligent automation to GitOps applications, AppSets, and clusters. Ask questions like "What applications are out of sync?" or "Which syncs failed in the past 24 hours?" and get instant answers drawn from your entire GitOps deployment landscape. The AI agent can also trigger operations—such as syncing all applications managing non-prod services with a single command or generating pipeline snippets for common GitOps workflows. This transforms dashboards and manual queries into conversational operations management, making GitOps accessible to platform teams, developers, and operators alike.

[Learn more about AI-powered GitOps →]

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Centralized Notifications for GitOps Applications

GitOps applications now integrate with Harness's centralized notification framework, bringing the same notification capabilities available for pipelines to your GitOps workflows. Track application sync events—start, complete, success, and failure—alongside ApplicationSet creation, sync, and error events through Slack, email, PagerDuty, Microsoft Teams, or any webhook-compatible system. Configure notification rules at the account, organization, or project level using the same interface you already use for pipeline notifications.

Learn more about GitOps notifications →

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Enhanced PR Workflows for GitOps

GitOps PR-based workflows get two key improvements. The Update Release Repo step can now block until the raised PR is merged, eliminating the need for separate Merge PR steps and manual approval stage coordination—the step creates the PR, waits for review, and proceeds once merged. Squash and Merge Support brings native squash-and-merge strategies to the Merge PR step, working with GitHub App tokens and following your repository's configured merge strategies to maintain a clean, linear repository history.

[Learn more about PR pipelines →]

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Get Started Today

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The features highlighted in this update are available now in Harness CD and GitOps. Ready to see them in action? We've created a comprehensive video playlist that walks through these capabilities, featuring live demos and configuration guides.

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Watch the Q1 2026 Feature Playlist →

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From AI-powered verification that understands your deployments from day one to Windows performance breakthroughs and GitOps workflow enhancements, this quarter delivers capabilities that eliminate configuration overhead, expand platform coverage, and align with how modern teams ship software.

Explore the documentation links throughout this post to dive deeper into each feature, or reach out to your Harness account team to discuss how these capabilities can accelerate your delivery workflows.

What's coming next? Q2 2026 will bring deeper integrations with cloud-native platforms, expanded AI capabilities across the deployment lifecycle, and continued investment in developer experience improvements. Stay tuned for more updates—we're just getting started.