Designing AI Agents: Research to Visualization with Felo Workflows

Artificial intelligence is shifting from static chat prompts to autonomous agents that can research, synthesize, and visualize information independently. Understanding how to orchestrate these workflows is becoming an essential skill for modern knowledge workers and developers alike. This text-only course guides you from the fundamental principles of agentic AI to deploying structured, automated research pipelines. By reading through clear explanations and practical design patterns, you will discover how to configure agents that gather data, verify facts, and present their findings in clean visual layouts. You will also explore how to integrate modern retrieval-augmented generation (RAG) concepts to ensure your agents produce accurate, grounded results without hallucination. What you'll learn: - Understand the core architecture and terminology of autonomous AI agents - Configure structured research workflows using Felo to automate data gathering - Apply retrieval-augmented generation patterns to improve informational accuracy - Design automated visualization rules to present complex research findings clearly - Manage state, memory, and decision-making loops within your agentic systems - Troubleshoot and refine agent prompts to handle edge cases and reduce errors The course begins with foundational definitions of agentic workflows and AI planning before moving step-by-step through practical configuration, data synthesis, and final visual output design. It is designed entirely for beginners, requiring no prior programming experience or advanced technical background. Start reading today and build your first autonomous research agent workflow.