In this demo, I build an autonomous AI agent for PDF processing—because when we talk about an agent, it has to do something on its own as part of a workflow. PDF extraction is one of those classic enterprise use cases: think KPMG, Deloitte, big consulting firms, finance teams—anyone sitting on a million invoices, tax documents, salary slips, and expense statements. I show you an end-to-end flow where you upload an invoice PDF, the system extracts the key fields (invoice number, details, etc.), and you’re immediately ready to plug that output into downstream business automation. Under the hood, I’m running FastAPI endpoints (so Swagger is built in), using Groq as the provider with an open-source model (Llama 3.3 70B Versatile), and a PDF client that leverages Llama Cloud to parse and extract the document. Then I combine LangChain-style agent reasoning with structured output generation—so you’re not just extracting text, you’re setting yourself up for categorization, expense reporting, and real automation. The big takeaway: extraction is step one; the real game is processing and organizing that data so an agent can handle scale—one PDF or a million.