AI Con USA 2026 - AI Real-World in Practice
Wednesday, June 10
Containers That Think: Building AI-Powered Self-Healing Applications That Never Go Down
Enterprise containerized applications face a critical reliability crisis with complex failure modes including memory leaks, cascading failures, network partitions, and resource contention that traditional monitoring tools cannot predict or resolve fast enough. Organizations typically experience multiple production incidents monthly with multi-hour resolution times that consume significant engineering resources while causing customer-facing outages and revenue loss. Traditional approaches rely on reactive monitoring, manual troubleshooting across distributed container environments, and time...
Beyond Generative AI: How Large Quantitative Models Are Transforming Scientific Discovery
PreviewScientific discovery faces a major bottleneck: while generative AI has transformed information processing, it has not yet unlocked comparable acceleration in understanding the physical world. Traditional computational chemistry and materials modeling remain too slow, expensive, and limited to power large-scale innovation. SandboxAQ tackled this challenge by developing Large Quantitative Models (LQMs) — a new class of AI models that merge machine learning, physics-based simulation, and quantum-inspired algorithms to model molecular and material interactions at unprecedented speed and...
Preparing for the Age of Physical AI: How Robot Learning Is Reshaping Manual Labor
Advances in robot learning and affordable general-purpose hardware are changing physical work faster than any past wave of automation. Industrial robot arms transformed repetitive factory tasks, but they could only operate in tightly controlled settings. Now, new learning methods and off-the-shelf mobile robots make it possible to teach machines to perform many kinds of hands-on work in warehouses and logistics facilities. This session looks at where the technology stands today, what has changed, and what the next few years will bring. Attendees will learn how to spot real opportunities...
Unlock Exponential Productivity: The AI Maturity Model for Product Engineering
Are you ready to transform your productivity from incremental gains to exponential growth? Whether you're an individual contributor or engineering leader, this session introduces the AI Maturity Model, a proven framework that guides technical teams through five distinct levels of AI adoption—from 33% productivity boosts to an extraordinary 1000% increase. Discover how to navigate each stage: Level 1 (Foundation, 33%) builds essential AI awareness; Level 2 (Literacy, 75%) develops practical AI skills; Level 3 (Fluency, 300%) masters AI-assisted workflows; Level 4 (Agents, 500%) implements...
Thursday, June 11
From Intelligence to Personalization: The New Competitive Frontier in AI
AI strategy has focused on intelligence—better models, stronger reasoning, and higher benchmark performance. That advantage is rapidly commoditizing. As AI becomes embedded across products and operations, the next source of durable value is personalization; systems that adapt to individual customers, employees, and contexts over time. Generic AI delivers diminishing returns; relevance, trust, and perceived quality increasingly determine adoption and impact. This talk explains why personalization is emerging as the decisive competitive frontier in AI, how it reshapes product strategy and...
Context Engineering for Agentic Workflows
The success of agentic applications using LLMs depends largely on the ability to properly manage the context - the collection of prompts, tools, history, memory, and RAG-indexed content. When you take all of these elements into account, you are able to get the most out of the LLM while avoiding hallucination, conversational drift, and relevance issues. Topics to be covered include: dynamic LLM selection, automated prompt development, context compression, tool decoration, RAG optimization, and LLM-as-a-judge quality assessment of LLM responses. Each of these smaller parts together build...