Discussion on agentic LLM adoption in production, focusing on LangChain and LlamaIndex tooling, production readiness, tools, evaluation and observability, safety and guardrails.

Dialogue on deploying agentic LLMs in production, focusing on LangChain and LlamaIndex usage, tools, evaluation and observability, safety and guardrails.

Components: planning, memory, tools, and environment; Frameworks overview: LangChain, CrewAI, LlamaIndex; The Agent Loop: perception → reasoning → action → feedback; Examples of orchestration and coordination.

Discussion on agentic LLM adoption, including LangChain and LlamaIndex in production, tools, evaluation and observability, safety and guardrails.

Agentic LLM adoption (LangChain/LlamaIndex in production, tools, evaluation and observability, safety and guardrails)

Agentic LLM adoption (LangChain/LlamaIndex in production, tools, evaluation and observability, safety and guardrails).

Dialogue 2: Agentic LLM adoption (LangChain/LlamaIndex in production, tools, evaluation and observability, safety and guardrails)

Agentic LLM adoption in production, including LangChain and LlamaIndex in production contexts, tools, evaluation and observability, safety and guardrails.

Dialogue on agentic LLM adoption, including production use cases, tooling and observability, safety and guardrails, with input from LangChain and LlamaIndex.

Discussion on LangChain and LlamaIndex in production, tools, evaluation and observability, safety and guardrails for agentic LLM workflows.

Discussion on agentic LLM adoption in production, featuring LangChain and LlamaIndex; topics include tools, evaluation, observability, safety and guardrails.

Agentic LLM adoption: LangChain/LlamaIndex in production, tools, evaluation and observability, safety and guardrails.

Agentic LLM adoption — LangChain and LlamaIndex in production, tools, evaluation and observability, safety and guardrails.

An overview of agentic AI concepts, focusing on how to build agentic apps with LlamaIndex. We'll cover core design patterns such as event-driven workflows, routing, parallelization, orchestrator–worker setups, and evaluator–optimizer loops, and show how to bring them to life in the LlamaIndex framework. The session also explores how these pieces fit into multi-agent systems, with a focus on MCP servers and tools that provide live context to agents. By the end, you'll learn to build agents using LlamaIndex, compose multi-agent systems, design reusable tools for agents, and give agents real-time knowledge. The talk uses the LlamaIndex Python framework and models from OpenAI and Anthropic.

An overview of agentic AI and how to build agentic apps using the LlamaIndex framework. Covers core design patterns such as event-driven workflows, routing, parallelization, orchestrator–worker setups, and evaluator–optimizer loops, and discusses multi-agent systems. The session will explore MCP servers and tools for providing live context to agents, and uses the open-source LlamaIndex Python framework with models from OpenAI and Anthropic.

An in-depth look at agentic AI — how to build agent-driven applications using the LlamaIndex framework. We’ll cover core design patterns such as event-driven workflows, routing, parallelization, orchestrator–worker setups, and evaluator–optimizer loops, and show how to implement them in LlamaIndex. The talk also explores how these pieces fit together into multi-agent systems, and how MCP servers and tools help agents obtain live context to hit their goals. By the end, you’ll learn to build agents with LlamaIndex, compose multi-agent systems, design reusable tools for agents, and give your agents real-time knowledge. The session uses the open-source LlamaIndex framework in Python and models from providers like OpenAI and Anthropic.

In this session, we present our experimental approach to creating DPK pipelines using agentic workflows. We will begin with a brief introduction to agentic workflows, followed by a walkthrough of two notebooks developed to support this work: The first notebook shows a planner agent for Data-Prep-Kit tasks with code generation. The agent builds DPK pipeline that performs required tasks defined by a natural language. The second notebook demonstrates how DPK transformers can be wrapped as tools within LangChain and LlamaIndex, along with examples of executing the transforms directly.

Overview: In this session, we present our experimental approach to creating DPK pipelines using agentic workflows. We will begin with a brief introduction to agentic workflows, followed by a walkthrough of two notebooks developed to support this work: the first notebook shows a planner agent for Data-Prep-Kit tasks with code generation, building DPK pipelines from natural language tasks; the second notebook demonstrates wrapping DPK transformers as tools within LangChain and LlamaIndex, with examples of executing the transforms directly.

In this session, we present our experimental approach to creating DPK pipelines using agentic workflows. We will begin with a brief introduction to agentic workflows, followed by a walkthrough of two notebooks developed to support this work:

The first notebook shows a planner agent for Data-Prep-Kit tasks with code generation. The agent builds DPK pipeline that performs required tasks defined by a natural language.

The second notebook demonstrates how DPK transformers can be wrapped as tools within LangChain and LlamaIndex, along with examples of executing the transforms directly.