The core challenge lies in the shift from deterministic application code to agentic models that make independent decisions. Because agents can choose different tools or paths for the same input, standard bug-fixing playbooks often fail. Harness aims to provide the same level of oversight for these agents that enterprises currently apply to standard software, ensuring compliance and operational control.
Harness Expands AI Lifecycle Management to Secure Agentic Workflows
Only 8% of organizations successfully move AI agents into production, largely because traditional software delivery pipelines cannot account for the unpredictable, non-deterministic nature of agentic code. Harness is now addressing this gap by extending its governance, security, and testing platform to manage the entire AI Agent Development Lifecycle.
To facilitate this, the platform introduces five core capabilities: AI Evals for measuring quality, Agent Deployments for managing releases across runtimes like Amazon Bedrock, AI Configs for runtime prompt management, an AI Asset Catalog for discovery, and AgentTrace for monitoring multi-step sessions. These tools integrate into existing pipelines, allowing teams to apply established security measures—such as primitive scanning and AI firewalls—directly to their AI workflows.
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