New short course: Event-Driven Agentic Document Workflows. Event-driven workflows are a key design pattern in which many pieces of work (LLM calls, tool use, etc.) can be carried out asynchronously and in parallel, and completion of specific steps generate events that trigger other work to begin. In this course, created in partnership with @llama_index and @seldo, VP of Developer Relations, you'll learn to apply this technique to document workflows.
Filling out complex forms can be tedious, time-consuming, and error-prone. Agentic workflows can automate this. This course teaches you how to build an agentic document workflow using an event-driven architecture.
You'll design an event-driven agentic workflow that fills out a PDF form based on information from a source document. The agent will use RAG to retrieve relevant data from the source document, parse the form to identify the required fields, convert the blank spaces into questions, and send those questions to the RAG system to fill the form. You'll collaborate with the agent using a human-in-the-loop feedback approach through text and with your voice.
In detail, you’ll:
- Understand the basic concepts of event-driven…
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
tool use — A model's ability to call external functions — run code, search the web, edit files — instead of only generating text.
Why it matters
Event-driven architecture is a concrete answer to serial AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → latency: parallel LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → calls and tool useA model's ability to call external functions — run code, search the web, edit files — instead of only generating text.Full definition → triggered by completion events, demonstrated on a real document task.
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