Directed Acyclic Graphs (DAGs) are the foundation of most orchestration frameworks. But what happens when you allow an LLM to act as the router? Acyclic graphs now become cyclic, which means you have to design for the challenges resulting from all this extra power. We'll cover the ins and outs of agentic applications and how to best use them in your work as a data practitioner or developer building today.
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Discover LangChain, the open-source framework for building powerful agentic systems. Learn how to augment LLMs with your private data, moving beyond their training cutoffs. We'll break down how LangChain uses "chains," which are essentially Directed Acyclic Graphs (DAGs) similar to data pipelines you might recognize from dbt. This structure is perfect for common patterns like Retrieval Augmented Generation (RAG), where you orchestrate steps to fetch context from a vector database and feed it to an LLM to generate an informed response, much like preparing data for analysis.
Dive into the world of AI agents, where the LLM itself determines the application's control flow. Unlike a predefined DAG, this allows for dynamic, cyclic graphs where an agent can iterate and improve its response based on previous attempts. We'll explore the core challenges in building reliable agents: effective planning and reflection, managing shared memory across multiple agents in a cognitive architecture, and ensuring reliability against task ambiguity. Understand the critical trade-offs between the dependability of static chains and the flexibility of dynamic LLM agents.
Introducing LangGraph, a framework designed to solve the agent reliability problem by balancing agent control with agency. Through a live demo in LangGraph Studio, see how to build complex AI applications using a cyclic graph. We'll demonstrate how a router agent can delegate tasks, execute a research plan with multiple steps, and use cycles to iterate on a problem. You'll also see how human-in-the-loop intervention can steer the agent for improved performance, a critical feature for building robust and observable agentic systems.
Explore some of the most exciting AI agents in production today. See how Roblox uses an AI assistant to generate virtual worlds from a prompt, how TripAdvisor’s agent acts as a personal travel concierge to create custom itineraries, and how Replit’s coding agent automates code generation and pull requests. These real-world examples showcase the practical power of moving from simple DAGs to dynamic, cyclic graphs for solving complex, agentic problems.