Sessions

Workshop

Agentic systems are emerging as a key paradigm in how we design intelligent applications that can reason, act, and adapt in dynamic environments. In this interactive workshop, Paige Bailey will introduce the foundational concepts behind agentic systems and demonstrate how to build them using CrewAI and Google’s Gemini APIs in Google Colab.

Through accessible, hands-on examples, attendees will learn how to structure basic agent workflows, call language models via API, and begin thinking about agent-based application design. This session is ideal for those new to the agentic AI landscape

Trianing

This 2-hour intensive session provides a foundational and practical guide to the burgeoning field of prompt engineering. As large language models become a core part of professional workflows, the ability to communicate with them effectively is crucial. This course will demystify the art and science of crafting powerful prompts.

Training

​In this event, we’ll explore the latest on agent evaluation from the leading LLM application evaluation framework: RAG ASsessment (RAGAS).​📚 You’ll learn: - How to think about assessing your agent applications quantitatively, with leading best-practice metrics - How agentic workflows are being assessed at the LLM edge​🤓 Who should attend the event: - Aspiring AI Engineers who want to build and evaluate production-grade agent applications - AI Engineering leaders who want to instrument their agent deployments with leading evaluators"

Training

This hands-on workshop equips you to solve real-world business problems with powerful LLMs. We'll compare frontier closed and open-source models, and discuss how to pick the right LLM for the task at hand. We’ll embark on an ambitious project to build an Agentic AI solution that beats frontier models. Our Agents will include a frontier model, a proprietary QLoRA fine-tuned LLM, and a RAG pipeline. There’s a lot to build, but we will draw on magical open-source libraries from HuggingFace, Gradio and Chroma to get it all done in time -- and the results will be rather satisfying.

Workshop

​ Agents, at least the good ones, don't follow the ""here's your prompt, here's a bag of tools, loop until you hit the goal"" pattern. Rather, they are comprised of mostly just software.

So, I set out to answer:

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?

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