
An AI agent is a system that uses a large language model to dynamically control an application's execution, with varying levels of autonomy based on the model's control over the process. The distinction between agents and workflows lies in the degree of control and flexibility, with agents offering greater autonomy and the need for robust tooling for observability and safe execution. The right level of autonomy depends on the task, tools, and team capabilities, and the Agent Development Lifecycle (ADLC) helps evaluate and improve agents through stages like build, test, deploy, and monitor.
Managed Deep Agents gives developers a managed way to build, run, and deploy Deep Agents with built-in runtime, streaming, sandboxes, evals, memory, and auth.

LangSmith Bring Your Own Cloud is now generally available on AWS, giving Enterprise teams managed observability, evaluation, and deployment inside their own VPC.

We benchmarked NVIDIA NeMo Switchyard on 145 agent tasks. Only 7% of turns needed a frontier model, and routing cut cost 74% for six points of accuracy.
