Harness, the AI Software Delivery Platform™ company, announced the extension of its enterprise software delivery ecosystem to govern the full AI Agent Development Lifecycle (DLC). The expansion provides enterprise engineering teams with a unified set of pipelines, security guardrails, and compliance controls to build, test, deploy, and operate autonomous AI agents using the same continuous integration and continuous deployment (CI/CD) methodologies applied to traditional software code.
The launch addresses a growing operational hurdle across corporate software engineering: the non-deterministic nature of AI agents. Unlike standard application code that generates predictable outputs from fixed logic, autonomous agents rely on underlying large language models (LLMs) to determine execution paths, select external tools, and trigger secondary workflows dynamically. According to internal industry benchmarks from Harness, while enterprise adoption of artificial intelligence continues to surge, only 8 percent of organizations have successfully deployed agentic AI into active production due to concerns over unpredictability, data exposure, and unmanaged execution paths.
Unifying Development Workflows for Non-Deterministic AI Systems
To bridge the gap between initial sandbox prototyping and secure production deployment, Harness Agent DLC integrates specialized capabilities directly into existing developer pipelines. Rather than requiring engineering teams to maintain fragmented evaluators, custom runtime firewalls, and separate monitoring stacks, the system applies policy enforcement across the entire agent lifecycle.
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The new product suite includes five core capabilities across testing, deployment, operations, and governance:
Harness AI Evals: Enables developers to establish evaluation datasets, scoring functions, and automated quality gates that measure agent performance across correctness, safety, and latency before code merges.
Agent Deployments: Extends enterprise canary releases, manual approvals, and Open Policy Agent (OPA) controls to managed agent environments—such as Amazon Bedrock AgentCore and Google’s Agent Runtime—eliminating cloud-specific deployment silos.
AI Configs: Decouples prompt engineering and model selection from application redeployments, allowing teams to test, update, or instantly roll back runtime configurations without rebuilding underlying code bases.
AI Asset Catalog: Automatically scans code repositories to index every active agent, skill, and plugin while assigning clear ownership to prevent shadow AI sprawl.
Harness AgentTrace: Captures step-by-step execution paths, tool calls, and model decisions across multi-turn sessions to streamline debugging and satisfy compliance audits.
Shift-Left Security and Runtime Shielding
Because agents expand corporate attack surfaces by executing tool calls, accessing external APIs, and spawning sub-agents, static security scans are insufficient. Harness Agent DLC embeds security primitives across every phase of execution. Pre-deployment checks include Primitive Scanning to catch skill misconfigurations, automated generation of an AI Bill of Materials (AIBOM), and adversarial testing against the OWASP Top 10 for LLMs. In production, the platform operates as a runtime firewall to block prompt injection attacks and halt data exfiltration attempts.
To support broad industry adoption, Harness is open-sourcing foundational tracing and evaluation components, including harness-sdk and harness-evals, allowing developers to integrate standard tracing primitives into custom AI applications.