Harness launches Agent DLC for governed AI delivery
Harness launched Agent DLC on July 21, extending its software-delivery platform with controls for testing, deploying, tracing, and governing AI agents. The company says the release adds AI Evals, Agent Deployments, runtime configuration management, asset discovery, AgentTrace, and design-time plus runtime security controls. Teams evaluating it should verify availability, integration depth, and control effectiveness because the retrieved sources provide no independent benchmarks or security tests.
What Harness launched
Harness introduced Agent DLC on July 21 as an extension of its software-delivery platform for building, testing, deploying, operating, and governing AI agents. The company says the capabilities are rolling out to Harness customers; its announcement does not provide pricing or a detailed entitlement matrix. Techzine also reported the launch and its focus on managing the agent lifecycle through existing delivery controls.
The release groups several new or expanded capabilities:
- •Harness AI Evals connects agent evaluations to delivery quality gates.
- •Agent Deployments extends delivery pipelines to managed runtimes, initially including Amazon Bedrock AgentCore and Google's Agent Runtime.
- •AI Configs manages prompt and model changes at runtime through feature-management controls.
- •AI Asset Catalog inventories agents, skills, and plugins and links them to owners.
- •AgentTrace records activity across an agent run and multi-step session. Harness says it is open-sourcing the underlying harness-sdk and harness-evals components.
Security before and after deployment
Harness describes a two-part security model. Design-time controls include Primitive Scanning for skills, prompts, and models; an AIBOM for models, tools, and dependencies; and adversarial testing against the OWASP Top 10 for LLM and agentic-AI risks. Runtime controls include agent discovery, posture management, and an AI Firewall intended to enforce policy against prompt injection, tool misuse, and data exfiltration.
These are vendor capability claims. The retrieved sources do not include independent penetration testing, comparative security results, or evidence that every control is available across every deployment environment. AgentTrace may improve investigation and auditability, but tracing alone does not establish that a policy prevented an unsafe action.
What buyers should verify
A useful evaluation should test whether organization-specific evals can block a release, whether prompt and model changes are versioned and reversible, and whether traces connect an action to the responsible model, tool, configuration, identity, and policy decision. Security teams should also confirm how the platform integrates with secrets management, data classification, incident response, and existing policy engines.
Availability matters as much as the feature list. Before adopting the platform, teams should request product-level documentation for supported runtimes, licensing, retention settings, export controls, and failure behavior, then validate those controls with representative agents rather than relying on the launch descriptions alone.
Key Points
- 1Harness Agent DLC adds evaluation, deployment, configuration, inventory, tracing, and governance controls for AI agents.
- 2The security layer combines design-time scanning and inventory with runtime discovery and firewall controls.
- 3The launch materials do not provide independent benchmarks, security tests, pricing, or a detailed availability matrix.
Scoring Rationale
Harness Agent DLC addresses a practical delivery and governance gap for teams deploying tool-using AI agents. The named evaluation, tracing, deployment, inventory, and security controls are relevant to platform teams, but the evidence remains a launch announcement and independent summary without comparative validation.
Sources
Primary source and supporting public references used for this report.
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