The second week of the AI Builders Summit has concluded, attracting participants globally to learn about maximizing AI agents. Expert instructors led sessions covering practical applications, including building AI agents using frameworks like LlamaIndex and techniques like retrieval-augmented generation (RAG). Key topics included the architecture of AI agents, effective workflows, and the integration of decision-making models. Attendees gained insights on designing autonomous systems and workflows for real-world applications. If you missed this week’s sessions, you can still sign up for upcoming workshops and access previous recordings on-demand. Join us as we dive into the future of AI and enhance your skills in this fast-evolving field.
We recently finished week 2 of our first-ever AI Builders Summit, and it was a huge success! Hundreds of participants joined us from around the globe. Our expert instructors guided attendees on how to maximize the potential of AI agents. If you missed out, no worries! You can still sign up for next week’s sessions or access the previous sessions on demand.
In our latest sessions, we covered a range of topics designed to empower developers in the AI space:
AI Agents – A Practical Implementation
Valentina Alto from Microsoft shared insights into the evolution of generative AI and how AI agents are changing the landscape. She provided a hands-on demonstration of building an AI agent that incorporates blockchain technology, memory, and knowledge bases.
Building Agentic RAG with LlamaIndex Workflows
Laurie Voss from LlamaIndex guided participants on optimizing workflows using the LlamaIndex framework. He demonstrated how multiple AI agents can collaborate to accomplish complex tasks, providing a step-by-step workshop on building a multi-agent system.
Exploring Modern AI Agents from A-Z
Sinan Ozdemir from LoopGenius tackled the foundational elements of agentic AI, emphasizing design principles and best practices for deploying AI agents in real-world applications. He discussed how developers can create adaptable systems using existing frameworks or building from scratch.
Using World Models for Optimal Decision-Making
Dr. Andre Franca introduced the concept of world models, demonstrating how agents can make informed decisions based on contextual knowledge. With a focus on reinforcement learning and hybrid models, he offered insights on improving decision-making processes.
LLM Engineering Masterclass
Edward Donner from Nebula.io provided a deep dive into selecting and deploying large language models (LLMs) for AI applications. His practical insights included setting up multi-agent workflows and a hands-on workshop for building an e-commerce AI assistant.
Practical Implementation and Evaluation of AI Agents
Presenters Greg Loughnane and Chris Alexiuk showcased how to implement AI agents in business applications. They focused on structured workflows and multi-agent collaboration, providing a live coding demo.
As we wrap up the AI Builders Summit on February 5th and 6th, we invite you to join us for a lineup of exciting workshops and demos focused on practical tools for building AI agents.
Don’t miss your chance to participate in these transformative sessions. Register now to catch both the upcoming sessions and access all past content on demand! For even more hands-on AI training, consider registering for ODSC East this May, which includes access to the AI Builders Summit.
Tags: AI Builders Summit, AI agents, LlamaIndex, world models, LLMs, AI training.
What were the main topics discussed in Week 3 of the AI Builders Summit?
In Week 3, the focus was on AI Agents. Various speakers shared their insights on how AI agents work, their applications, and how they can improve business processes.
How can AI agents benefit businesses?
AI agents can automate tasks, enhance customer service, and help with decision-making. This means companies can save time and resources, leading to increased efficiency.
What examples of AI agents were mentioned at the summit?
Some examples included virtual assistants, chatbots for customer support, and AI-driven analytics tools that help businesses understand their data better.
Are there any challenges with using AI agents?
Yes, challenges include data privacy concerns, the need for continuous training, and the potential for AI to make mistakes. Companies must carefully manage these issues to ensure effective use.
How can someone start using AI agents in their business?
To start using AI agents, businesses should identify tasks that can be automated, choose the right tools, and train staff to work with AI. It’s also important to monitor performance and adjust strategies as needed.
