Data Science Dojo – New York

## Understanding Deep Agents: The Future of AI Autonomy

The next evolution of AI agents is here. Deep Agents move beyond simple tool-calling LLMs into powerful, stateful systems that can reason over time, collaborate across sub-tasks, and deliver reliable results in real applications. This session will break down what Deep Agents are, why they matter, and how LangGraph makes them practical to build today.
We’ll explore the limitations of traditional agents that lose context, fail on long-running tasks, or collapse without human intervention. Then we’ll introduce the four pillars of Deep Agents: planning, sub-agents, memory and state, and a virtual file system that enables durable workflows.
A live walkthrough will show how LangGraph helps developers orchestrate scalable, production-ready Deep Agents with human oversight, observability, and debugging built in. You’ll learn how to structure persistent reasoning, delegate tasks effectively, and maintain state across complex workflows.

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### What We Will Cover:

• Why traditional agents fall short on multi-step, long-running tasks
• The architecture of Deep Agents and how each pillar supports persistent reasoning
• How LangGraph enables stateful agents with feedback loops, scaling, and error recovery
• Building a Deep Agent step-by-step — planning, delegation, and memory management
• Real-world use cases from research automation to decision support systems
• Key considerations and safety mechanisms when deploying Deep Agents in production

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### Hands-On Insights:

Through examples and Q&A, participants will learn how to start building Deep Agents in their own environments using LangGraph. You’ll leave with the mental model, tools, and practical patterns to evolve your agent systems from simple demos into durable, intelligent applications.