Oct 13, 2026

Automating document work with Long-Horizon AI Agents at LlamaIndex

Jerry, CEO of LlamaIndex, argues that as AI agents move from answering questions to running long-lived workflows, documents become their most important context source. He walks through the evolution from simple RAG to agent harnesses, explains why PDFs are hard to parse, and presents the company's approach to OCR, extraction with grounding, and document search. He closes by outlining unsolved problems: agent-native document formats, in-place editing, versioning, distilling frontier workflows into cheaper models, and long-horizon background agents.

Oct 07, 2026

Grep or Embeddings? Agentic Search Over Company Documents — George He, LlamaIndex

George He, head of engineering at LlamaIndex, explores when agents need pre-indexed vector search and when plain file traversal with grep is enough. He contrasts Claude Code's embedding-free approach on code with the harder reality of large, messy, multimodal, permissioned company documents, and proposes a harness of hybrid retrieval, metadata filtering, grep, and reading tools backed by LlamaParse and LiteParse. The talk closes with a live demo of an agent building a cash-flow table from 135 Alphabet filings, grounded in source files.

Jul 09, 2026

Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production

This video tackles the overwhelming choice of agentic AI frameworks by categorizing projects into five types: linear workflows, autonomous multi-agent systems, role-based AI, production orchestration, and rapid prototyping. It details each type with examples and recommends specific frameworks like LangChain, AutoGen, and CrewAI, guiding developers to select the optimal tool based on their system design and real-world needs.

Sep 15, 2025

Building Advanced Agents Over Complex Data // Jerry Liu

Jerry from LlamaIndex explains why naive Retrieval-Augmented Generation (RAG) fails in production and dives deep into advanced data quality techniques—from parsing complex documents and hierarchical indexing to chunking best practices—required to build robust, high-quality LLM applications.