Automating Document Work with Long-Horizon AI Agents at LlamaIndex

Most of enterprise data is unstructured, and a lot of that lives in the form of documents - PDFs, pptx, docx, etc. - powering a majority of enterprise knowledge work. AI agents have the potential to simultaneously better make sense of docs and also automate human workflows over them. Existing document AI use cases have either been long-running but constrained (e.g. a batch invoice processing through RPA), or non-deterministic but short-horizon (e.g. ChatGPT deep research over a collection of existing files). Going forward there's a huge opportunity to build long-running agents that can understand, reason over, and edit documents at scale. In this session, Jerry Liu, Co-Founder and CEO of LlamaIndex, will walk through core advances in document OCR and agent orchestration that have given rise to initial use cases around doc extraction workflows and general-purpose chatbots (and share real-world customer use cases). You'll also see some of the remaining gaps needed to achieve "long-running" document work automation.