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Agentic engineering is a multi-agent coordination model where AI agents act as digital team members, focusing on orchestrating cross-team workflows, maintaining long-term memory, and ensuring traceability across the software delivery lifecycle. It differs from single-agent systems like Codex or Claude by operating at a higher level of abstraction, emphasizing collaboration, state management, and observability. Pilot studies showed significant improvements in efficiency, with a 93% reduction in time-to-root-cause and over 200 engineering hours saved across debug sessions. The system uses LangGraph and LangSmith for state and execution traces, enabling scalable, autonomous workflows that mirror real-world engineering practices.
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.

Learn what AI agents are, how they work in an LLM loop, and where workflows fit so you can build reliable, production-ready autonomous systems.
