Harness Extends AI Software Delivery Platform to Cover the Full AI Agent Development Lifecycle

SAN FRANCISCO – Harness, a leading innovator in AI Software Delivery Platforms, today announced a significant expansion of its core offerings, now encompassing the entire AI Agent Development Lifecycle (DLC). This strategic move provides enterprises with a unified suite of pipelines and controls, enabling them to build, test, deploy, and manage AI agents with the same rigor and confidence they apply to traditional software applications. This development addresses a critical bottleneck hindering widespread AI agent adoption, as the vast majority of organizations struggle to move beyond pilot programs and proofs of concept.

The burgeoning field of AI agents promises to revolutionize business operations, automating tasks and enhancing decision-making across various departments, from engineering and product development to sales and customer support. However, realizing this potential has been hampered by the absence of mature, enterprise-grade development and deployment frameworks. According to Gartner, a prominent industry analyst firm, a stark reality exists: "Only 8% of organizations have agentic AI in production." This statistic underscores a significant gap between the promise of AI agents and their practical implementation within enterprise environments.

The traditional software delivery lifecycle, honed over decades to manage the complexities of application code, has proven insufficient for the unique challenges presented by AI agents. The deterministic nature of conventional software, where identical inputs consistently yield identical outputs, allows for predictable testing, debugging, and deployment. AI agents, however, operate on a fundamentally different paradigm. Their underlying language models introduce an element of inherent unpredictability. The same agent, presented with the identical prompt or task, might select a different tool, execute a varied action, or generate a distinct response on subsequent runs. This non-deterministic behavior renders traditional testing methodologies inadequate, making it difficult to ensure consistent performance and identify the root causes of failures.

"When we started Harness, our vision was to create a safety harness for code," stated Jyoti Bansal, co-founder and CEO of Harness. "Historically, that meant securing and streamlining the delivery of application code. Today, that vision extends to agentic code, which is being developed by teams across engineering, product, sales, and support, each building agents to optimize their specific workflows. All the discipline, governance, orchestration, security, and testing practices that enterprises have established for software delivery over the past decade can now be applied to AI agents within the same integrated platform."

The Unpredictability of AI Agents and the Breakdown of Traditional Lifecycles

The core challenge in managing AI agents lies in their inherent stochastic nature. Unlike traditional software, where a bug is a specific error in code that can be pinpointed and fixed, an agent’s "bug" might manifest as an unexpected or suboptimal output from its language model. This makes incident reproduction a formidable task, as the conditions leading to an error may not be easily replicated. Consequently, the established playbooks for bug detection and resolution, which are fundamental to traditional software development, are not directly transferable.

The stakes for enterprises are significantly elevated by the scale and potential impact of AI agents. A misbehaving agent could inadvertently expose sensitive customer data, violate regulatory compliance mandates, or execute unauthorized actions. Without robust governance and oversight mechanisms, organizations face considerable risks. The traditional software delivery lifecycle was never designed to provide the necessary audit trails, accountability, and control required for these dynamic AI systems. This gap in tooling and process has created a significant barrier to entry, preventing many organizations from realizing the full return on their internal AI investments. The ability to deploy and operate AI agents with the same level of trust and assurance as existing software applications is paramount for unlocking their true value.

Harness Agent DLC: Bridging the Gap to Production Readiness

The introduction of Harness Agent DLC aims to bridge this critical gap, providing a comprehensive solution for moving AI agents from development to safe and reliable production environments. The new suite of products and capabilities spans key areas of the development and operational lifecycle, including testing, deployment, operations, and governance.

Key components of the Harness Agent DLC launch include:

• AI Evals for Testing: This capability allows teams to define evaluation datasets, configure sophisticated scoring functions, and establish automated quality gates. These gates can automatically detect regressions when an agent or its underlying model undergoes changes, directly addressing the non-deterministic nature of LLM-based agents. This ensures that even with varied outputs, the agent’s performance against defined criteria remains consistent.
• Agent Deployments: This feature extends Harness’s proven canary release strategies, approval workflows, and Open Policy Agent (OPA) policy guardrails to managed agent runtimes. This includes support for popular platforms like Amazon Bedrock AgentCore and Google Agent Runtime. Agents can now be deployed through existing Harness pipelines, bypassing the need for cloud-provider-specific workflows and ensuring a consistent deployment experience.
• AI Security: Harness is introducing new security capabilities designed to address the unique vulnerabilities of AI agents. These include "shift-left" measures to constrain agent behavior before deployment and "shield-right" measures to enforce policies and maintain visibility once agents are live.

Furthermore, Harness is leveraging its existing product suite to provide immediate value to AI agent development:

• Continuous Integration (CI): Agents can be built using the same CI pipelines as other services, ensuring a familiar and integrated development workflow.
• Artifact Registry: Versioning and dependency tracking for AI agents are managed seamlessly within Harness’s Artifact Registry, providing a clear lineage and control over agent components.
• AI Test Automation: This existing product can validate agent responses against plain English criteria, offering a more intuitive approach to testing LLM-generated content.
• Cloud AI Cost Management: Harness extends its cost visibility to encompass every AI agent and model, providing crucial insights into the financial implications of AI deployments.

Fortifying AI Agents: A Dual Approach to Security

The dynamic and self-directed nature of AI agents presents significant security challenges. Their ability to interact with external tools and APIs, spawn sub-agents, and inherit trust from multiple models expands their attack surface in ways that traditional security scanning methods were not designed to handle. Static analysis, which is effective for conventional code, falls short when faced with the emergent behaviors of AI agents.

Harness is addressing these vulnerabilities through a two-pronged security strategy:

Shift-Left Security: Proactive Risk Mitigation

This approach focuses on identifying and mitigating potential risks early in the development lifecycle. New "shift-left" security capabilities include:

• Primitive Scanning: Identifies misconfigurations in agent prompts and configurations that could lead to vulnerabilities.
• AIBOM (AI Bill of Materials): Provides comprehensive tracking of all dependencies, including models, tools, and data sources used by an agent, similar to a software bill of materials. This enhances transparency and aids in identifying potential supply chain risks.
• AI Testing: Specifically targets common AI and LLM vulnerabilities, including those outlined in the OWASP Top 10 for LLM Applications and Agentic AI risks. This includes testing against prompt injection, data leakage, and unauthorized tool usage.

Shield-Right Security: Continuous Monitoring and Enforcement

Once agents are deployed, continuous monitoring and policy enforcement are crucial. Harness’s "shield-right" capabilities include:

• Agent Discovery and Posture Management: Provides runtime visibility into deployed agents, their interactions, and their overall security posture. This allows for the identification of rogue or misbehaving agents in real-time.
• AI Firewall: Acts as a real-time enforcement mechanism, actively blocking malicious activities such as prompt injection attacks, unauthorized tool usage, and data exfiltration. This provides a critical layer of defense against emerging threats.

Collectively, these security measures establish a single, auditable trail from agent development through to its production runtime, offering unprecedented transparency and control.

The Harness Platform: A Unified Foundation for Software and Agents

The Harness platform’s strength lies in its integrated architecture and the concept of the Software Delivery Knowledge Graph. This graph captures and connects data from every stage of the delivery lifecycle, now spanning both traditional applications and AI agents. This holistic view is a significant advantage over organizations relying on disparate, siloed tools, which often lack the necessary context to understand the complete picture of their software and AI deployments.

This unified approach builds upon Harness’s earlier innovations, such as the introduction of Autonomous Worker Agents in June 2026. These agents are designed to run as governed steps within software delivery pipelines, benefiting from the same robust controls that Harness applies to every deployment.

The extension of the platform to encompass the full Agent DLC means that established governance, policies, approval workflows, and evidence trails are now consistently applied to AI agents. Critical steps such as evaluation gates, deployment approvals, and security checks can be seamlessly integrated as stages within a single, cohesive pipeline, from the initial creation of an agent through its entire operational lifecycle. This ensures that the same level of rigor applied to shipping critical business applications is now available for the development and deployment of AI agents.

Availability and Broader Implications

The new Harness Agent DLC capabilities are currently rolling out to Harness customers. A comprehensive overview of the features available at each stage of the lifecycle can be found on the Harness blog.

The implications of this development are far-reaching. By providing a standardized and secure framework for AI agent development and deployment, Harness is empowering enterprises to accelerate their AI initiatives. This move is expected to:

• Democratize AI Agent Development: By simplifying the process and providing familiar tools, more teams within an organization will be able to contribute to AI agent development, fostering innovation.
• Reduce Time-to-Market for AI Solutions: Streamlined pipelines and automated testing will significantly shorten the cycle from idea to production for AI-powered applications.
• Enhance Trust and Reliability: The focus on governance, security, and testing will build greater confidence in the performance and safety of AI agents, leading to wider adoption.
• Optimize AI Investments: Integrated cost management and performance monitoring will help organizations better understand and control the ROI of their AI initiatives.
• Set New Industry Standards: Harness’s comprehensive approach to the AI Agent DLC may well establish a new benchmark for how enterprises approach the development and deployment of AI-powered systems.

As AI agents become increasingly integral to business operations, the need for robust, secure, and manageable development and deployment processes will only grow. Harness’s expansion into the full AI Agent DLC represents a critical step towards realizing the transformative potential of AI in a responsible and scalable manner.