Enterprise AI Agents Defined by Runtime Infrastructure, Not Just Models

A new analysis from Focused Labs argues that enterprise AI agents should be understood as runtime products, where the real value lies in code, credentials, security boundaries, and audit trails rather than the underlying model. The piece highlights the LangChain and NVIDIA NemoClaw Deep Agents Blueprint as a concrete example, combining Nemotron 3 Ultra, OpenShell, and LangChain tooling into a unified agent system with defined permission and evaluation layers. NemoClaw's coding agent runs inside an OpenShell sandbox with network egress denied by default, credentials excluded from the sandbox, and per-session audit logging built in. The authors contend that enterprises are not buying an 'agent' but a governed product surface on which an agent can operate without breaching policy. The harness layer — not the model itself — is emerging as the critical lock-in point, since it holds all runtime dependencies that are difficult to replace once a system is live.