Agents are transforming how we approach problem-solving, automation, and user interaction. In this talk, I will explore the practical applications of agents, focusing on how they can deliver value. We'll discuss when agents are the right tool for the job, scenarios where they are not the right tool for the job, and strategies for deploying them to production with confidence and reliability. Whether you're new to agents or looking to refine your approach, this session offers actionable insights grounded in real-world experience.

AI Agents as Neuro-Symbolic Systems: Expanding the Boundaries of Intelligence" The current discourse around AI agents often centers on LLM-based systems with tool-calling capabilities, like REACT agents. While effective, this narrow definition limits the potential of agents to solve complex, real-world problems. In this talk, we explore a broader, more robust perspective—AI agents as neuro-symbolic systems. By combining neural networks' adaptability with the precision of symbolic reasoning, neuro-symbolic architectures bridge traditional AI approaches and modern advancements, enabling scalable and versatile workflows. This expanded definition accommodates not only LLMs but also embedding models, decision trees, and hybrid systems that integrate various modalities of intelligence. We will delve into: 1. The evolution of AI agents and the limitations of current models. 2. The core principles of neuro-symbolic systems and their practical applications. 3. A reimagined framework for building intelligent agents that operate flexibly across diverse tasks. This session aims to redefine the way we think about AI agents, empowering developers and researchers to design systems that are more efficient, resilient, and capable of tackling dynamic challenges. Join us as we explore the future of agentic AI and its transformative potential.

This blog highlights 20 ways generative AI (GenAI) can enhance technical documentation. It covers key use cases like drafting structured documents, generating API examples, ensuring content consistency, improving search functionality, and automating quality control. AI tools can assist in creating templates, summarizing release notes, generating glossary terms, detecting bugs in code snippets, and optimizing search queries. The blog emphasizes that while AI is a powerful tool, it should be used wisely to complement, not replace, the work of technical writers and developers, helping to produce better, more reliable docs that build trust with users.

Qualcomm® AI Hub helps to optimize, validate, and deploy machine learning models on-device for vision, audio, and speech use cases. With Qualcomm® AI Hub, you can: Convert trained models from frameworks like PyTorch and ONNX for optimized on-device performance on Qualcomm® devices. Profile models on-device to obtain detailed metrics including runtime, load time, and compute unit utilization. Verify numerical correctness by performing on-device inference. Easily deploy models using Qualcomm® AI Engine Direct, TensorFlow Lite, or ONNX Runtime. The Qualcomm® AI Hub Models repository contains a collection of example models that use Qualcomm® AI Hub to optimize, validate, and deploy models on Qualcomm® devices. Qualcomm® AI Hub automatically handles model translation from source framework to device runtime, applying hardware-aware optimizations, and performs physical performance/numerical validation. The system automatically provisions devices in the cloud for on-device profiling and inference. The following image shows the steps taken to analyze a model using Qualcomm® AI Hub.

Demetrios chats with Zach Wallace, engineering manager at Nearpod, about integrating AI agents in e-commerce and edtech. They discuss using agents for personalized user targeting, adapting AI models with real-time data, and ensuring efficiency through clear task definitions. Zach shares how Nearpod streamlined data integration with tools like Redshift and DBT, enabling real-time updates. The conversation covers challenges like maintaining AI in production, handling high-quality data, and meeting regulatory standards. Zach also highlights the cost-efficiency framework for deploying and decommissioning agents and the transformative potential of LLMs in education.

//Abstract AI has transformed industries, yet its true potential often lies untapped within core business processes. In this session, we’ll explore how AI agents differ from generative AI models, emphasizing their deterministic, hallucination-free approach to problem-solving. We’ll take a live example of an AI Agent in the logistics sector, and will detail the architectural foundations that enable AI agents to reason effectively, execute chain-of-thought workflows, and integrate seamlessly into human teams. We’ll discuss how these agents confidently navigate complex, multimodal tasks, extracting structured insights from unstructured data, and leveraging dynamic workflows for maximum flexibility. With customizable confidence thresholds, statefulness to track long-term cases, and advanced document understanding, these agents solve real business challenges, such as processing autonomously claims till resolution, with precision. Through a live case study, we’ll illustrate the measurable top and bottom-line effects of deploying AI agents—highlighting significant efficiency gains, multilingual capabilities, and safe, scalable applications in mission-critical environments. By showcasing how AI agents mimic human decision-making at unparalleled speed, we’ll inspire senior management to rethink AI’s role in their organizations and harness its full potential for transformative impact. //Bio Passionate about connecting deep tech to end-users, Vanessa’s work is at the forefront of AI’s transformative potential. For over a decade, she has been transforming cutting-edge innovations into actionable solutions that drive industry change. This is a bi-weekly "Agent Hour" event to continue the conversation about AI agents. Thanks to arcade-ai.com for the support! Join the next live event at home.mlops.community

//Abstract Demonstrating agents embedded within websites that utilize real-time audio and structured outputs to dynamically update web pages through conversational interactions. //Bio Raised in Reykjavík, living in Berlin. Studied computational and data science, did R&D in NLP and started making LLM apps as soon as GPT4 changed the game. This is a bi-weekly "Agent Hour" event to continue the conversation about AI agents. Thanks to arcade-ai.com for the support! Join the next live event at home.mlops.community

Demetrios chats with Egor Kraev, principal AI scientist at Wise, about integrating LLMs to enhance ML pipelines and humanize data interactions. Egor discusses his open-source MotleyCrew framework, career journey, and insights into AI's role in fintech, highlighting its potential to streamline operations and transform organizations.