Richard
Richard Kehinde Ogunyale is a Senior Software Engineer based in London, UK, with experience building production AI systems, scalable microservices, and machine learning pipelines. He currently works at Partnerize, where he leads projects involving AI-powered solutions, and has previously built RAG systems with vector databases, LLM-powered automation workflows using DAG architectures at scale.
He is passionate about open source, practical AI engineering, and bridging the gap between ML prototypes and reliable production systems.
Session
AI systems that can autonomously navigate websites, fill forms, extract data, and complete multi-step workflows; are one of the most exciting and practical applications of large language models in 2026. Libraries like browser-use (60k+ GitHub stars) and Skyvern have demonstrated their potential, but their abstractions can obscure the surprisingly approachable fundamentals underneath.
In this 90-minute hands-on tutorial, attendees will build a browser agent entirely from scratch using only Python, Playwright, and an LLM API. No agent frameworks, no magic; just the core building blocks: extracting and structuring the DOM into an LLM-friendly representation, capturing screenshots for vision-based reasoning, building the observe-think-act agent loop, and handling real-world challenges like dynamic content, multi-tab navigation, and error recovery.
By building from first principles, attendees will gain a deep understanding of how browser agents actually work; knowledge that transfers directly to using, debugging, and extending any browser agent framework. Every participant will leave with a working agent that can autonomously complete tasks on live websites.
This tutorial is aimed at Python developers and data scientists who are curious about AI-driven browser automation. Basic Python proficiency and familiarity with async/await are expected. No prior experience with Playwright, browser automation, or agent frameworks is required.