Step into the future of AI with an immersive dive into the cutting-edge world of Edge Gen AI and Quantum Teleportation technologies. This session explores the intersection of multi-threaded AI workflows and the powerful anomaly detection capabilities made possible by Quantum-inspired computing.

Discover the latest trends in Gen AI ModelOps, where agile deployment strategies for domain-specific generative AI models are accelerating innovation in industries ranging from healthcare to defense. We’ll uncover how GraphRAG (Graph Retrieval-Augmented Generation) is revolutionizing data interaction, enabling real-time knowledge graphs to enhance AI inference and decision-making.

Explore Poly AI, a framework pushing the boundaries of multi-modal and multilingual AI systems, delivering unprecedented versatility across languages, data types, and platforms. Learn how these advancements are reshaping AI landscapes, paving the way for hyper-customized, scalable solutions for domain-specific applications.

Join Jonathon for this exciting journey, where quantum principles, AI, and domain-specific intelligence converge to redefine the future of automation, knowledge discovery, and beyond. Expect to walk away with actionable insights and the latest strategies for leveraging GenAI in your enterprise's edge environments.

Agentic systems represent a new software paradigm that enables products to act autonomously, adapt to dynamic environments, and make decisions in real-time. However, building production-grade agentic systems requires addressing key challenges in reliability, safety, and performance. In this session, we explore the core principles and best practices for designing robust, scalable, and trustworthy agentic solutions.
Creating the right product is a cross-functional effort—engineering, product and design must evolve their processes to harness the power of agentic AI. We outline a practical roadmap for upskilling teams, fostering AI literacy, and integrating agentic capabilities across disciplines. Through real-world examples, we demonstrate how collaboration between technical and non-technical stakeholders can accelerate AI adoption while mitigating risks.
Beyond technical implementation, organizations must undergo a fundamental shift to support agentic products at scale. We discuss key organizational transformations, including restructuring workflows, redefining success metrics, and cultivating an AI-first culture. Whether you're just starting your AI journey or looking to enhance existing capabilities, this session provides actionable insights on kickstarting agentic initiatives within your organization.

In the rapidly evolving landscape of AI, separating genuine advancements from the noise is crucial for both industrial practitioners and academic researchers. The non-deterministic nature of LLM-driven applications amplifies this challenge, leading to difficulty in applying common software testing practices and thereby quantifying application performance.

This session aims to offer insight into mitigating these issues by delving into RAGAS (Real-World Application and Generalization Assessment Score), a novel framework designed to quantitatively measure the performance of LLM-driven applications. Drawing from well-established practices in the software testing world, it demonstrates how RAGAS can guide the development and deployment of LLM technologies with tangible benefits. A short demo is also included as a practical demonstration of how RAGAS's ability to evaluate LLM effectiveness can be translated into real-world impact.

This immersive workshop is designed for artificial intelligence engineers, professionals, testers, and developers who are eager to leverage the cutting-edge capabilities of AI to enhance their engineering strategies, automate processes, and uncover new opportunities with unparalleled accuracy. By blending theoretical knowledge with practical exercises, participants will gain firsthand experience in harnessing the power of AI-Augmented to transform their approach to software engineering, making it more efficient, effective, and innovative.

Throughout the workshop, attendees will explore the core concepts of generative AI and its application in software engineering. Through a series of hands-on exercises, interactive sessions, and live demonstrations, attendees will learn how to integrate these AI-powered tools and techniques into their daily workflows, elevating the quality of their productivity and the products they help to create.

This immersive workshop is designed for artificial intelligence engineers, professionals, testers, and developers who are eager to leverage the cutting-edge capabilities of AI to enhance their engineering strategies, automate processes, and uncover new opportunities with unparalleled accuracy. By blending theoretical knowledge with practical exercises, participants will gain firsthand experience in harnessing the power of AI-Augmented to transform their approach to software engineering, making it more efficient, effective, and innovative.

Throughout the workshop, attendees will explore the core concepts of generative AI and its application in software engineering. Through a series of hands-on exercises, interactive sessions, and live demonstrations, attendees will learn how to integrate these AI-powered tools and techniques into their daily workflows, elevating the quality of their productivity and the products they help to create.

Agentic Systems and extensible compound AI systems are revolutionizing LLM applications, positioning themselves as critical tools in modern AI development. These advanced systems go beyond traditional automation, offering capabilities that drive significant productivity and efficiency gains in enterprise and commercial workflows. However, adopting AI Agents and Agentic Systems at scale poses unique challenges, particularly in ensuring consistent performance, reliability, and scalability.

Central to overcoming these challenges is the role of memory. Memory within AI systems is not only essential for retaining operational data but also for enabling adaptive learning, entity profiling, and customized interactions. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent’s functionality. This talk will delve into the architecture of Agentic Systems and examine how various forms of memory—working memory, data stores, profilers, and toolboxes—contribute to creating robust, efficient, and scalable AI solutions. Attendees will gain insight into how memory is leveraged to enable learning from past executions, personalize interactions, and enhance system capabilities in complex AI applications.

Generative AI have become essential in advancing AI, enabling remarkable capabilities in natural language processing and understanding. However, the efficient deployment of LLMs in production environments reveals a landscape of challenges and technical debt

Ethically, LLMs face issues such as bias amplification, where they might perpetuate existing stereotypes in their outputs. Misinformation is another concern, with the potential misuse of LLMs to create convincing yet false narratives. Privacy risks emerge from LLMs possibly memorizing and revealing personal data. Moreover, societal challenges include the impact on employment, as LLMs could automate tasks but also lead to job displacement. These challenges highlight the need for careful management and ethical considerations in the deployment of LLMs

In this talk, Ahmed will highlight the key challenges and technical debt associated with LLMs' deployment, which demands customization and sophisticated engineering solutions not readily available in broad-use machine learning libraries or inference engines