AI Con USA 2026 - AI Application Development
Monday, June 8
Getting Started with AI and Machine Learning
Are you a software professional who would like to learn to use AI and machine learning (ML), but don't know how to get started? One of the best ways to get into ML is by designing and completing small projects. Although you will ultimately need to understand the fundamentals of AI/ML, there's no reason why you can't learn foundational terms, concepts and principles as you put them into practice. Join Dionny Santiago as he introduces you to the world of applied machine learning. Dionny will guide you through a series of ML projects end-to-end, enabling you to gain experience with creating...
Model Context Protocol Fundamentals
NewTired of staring at the chat box of an AI assistant wishing it could actually do a task for you rather than just telling you what to do? Model Context Protocol (MCP) is the glue between an AI agent and your data and tools. With MCP, AI assistants can read your documents, conversations, and other context and take action for you by sending emails, submitting tickets and more. But what really is MCP and how do you use MCP servers safely in practice as part of your day-to-day workflow? This half-day course introduces MCP and dives into practical use cases. Laurel will walk you through how to...
A Quality Engineering Introduction to AI and Machine Learning
Although there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security,...
Become an AI Power User
Impostering a bit in the AI-verse? Overwhelmed by daily AI announcements? Unsure you're using AI most effectively? Tiny bit of FOMO? We've got you covered! In this workshop, we'll help you become an AI Power User. Become a boss at your job, whatever your role or industry! We'll show you where AI shines and where you'll want to be careful, plus toss you lots of hands-on practice. In our time together, we'll help you pinpoint YOUR niche, build a custom AI assistant, and develop a comms strategy to show off your new skills. You'll walk out with cutting-edge knowledge, a prompt library...
Tuesday, June 9
AI-Enabled Building: Let the Robot Do the Work
NewTheory is great, but what about getting your hands dirty with some real problem-solving? Lets build your own AI Operating System that you'll take home and use immediately. Join Melissa Benua and Ryan Lee for a hands-on session where you'll construct a personalized AI toolkit that solves YOUR specific testing problems, regardless of your coding background. We'll start by deconstructing the modern software development lifecycle used by all tech companies to safely ship software solutions to their customers, and show you exactly where AI accelerates each phase. Then we'll prove a critical...
Strategies for Testing Autonomous AI and Multi-Agent Architectures
NewTesting Artificial Intelligence (AI) agents presents a paradigm shift from traditional software quality assurance. Unlike deterministic, rule-based applications, AI agents exhibit emergent behaviors, learn from their environments, and make autonomous decisions, making conventional test case design and execution insufficient. This tutorial will provide a comprehensive understanding of the unique challenges and advanced strategies required to effectively test single and multi-agent AI systems. Participants will learn how testing agents differ significantly from testing traditional software....
AI Deep Dive: Exploring AWS Using Real-World Scenarios
Deepen your AI and machine learning expertise using AWS in an Immersive, hands-on workshop. You’ll use real-world AI challenges while leveraging AWS services like Amazon SageMaker, Bedrock, and Lambda to build and optimize AI-driven solutions. As the session unfolds, new constraints and data anomalies will emerge, mirroring the complexities of real-world AI/ML implementation. Gain insight into how AI solutions perform under evolving conditions, learning to adapt, optimize, and troubleshoot unexpected challenges. Learn the importance of collaboration, strategic thinking, problem-solving,...
Vibe-Coding a Business: Using AI to Rapidly Build, Test, and Validate Real Products
NewAI has fundamentally changed how quickly new businesses can be created. With modern, high-end models, individuals and teams can now move from a raw idea to a functioning product, market narrative, sales motion, and operating loop in days rather than months. The challenge is no longer whether AI can generate assets—it’s how to use AI intentionally across the entire business lifecycle to produce outcomes that are relevant, credible, and sustainable. In this hands-on tutorial, Jason Arbon leads participants through a practical, end-to-end approach to “vibe-coding” a business using AI. The...
Wednesday, June 10
Architecting the Digital Work System: The Shift to Multi-Agent Enterprise Workflows
The era of single-turn AI prompting is over, giving way to the sophisticated "digital assembly line." This session guides technical leaders, CTOs, and AI product managers through the critical leap from isolated AI tools to coordinated, multi-agent constellations capable of autonomously executing complex enterprise workflows. Marshall will dissect the four levels of agentic evolution, focusing on today's epicenter of innovation: cross-system orchestration. Because the risk of cumulative failure multiplies with every automated handoff, he will unpack the architectural strategies required to...
AI-Native Delivery: Why the Future Belongs to Those Who Specify and Validate
AI in software delivery is no longer just about experimenting with copilots or generating code faster. It is about changing how software is imagined, specified, generated, validated, governed, and improved. As AI continues to be used for producing prototypes, requirements, code, tests, documentation, deployment artifacts, and operational insights, the software lifecycle shifts from a sequence of handoffs into a continuous learning loop. The real opportunity is therefore not only about leveraging AI tools, but adopting a new working model for transforming intent into trusted outcomes. Join...
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...
Governance for Fast-Moving AI: Securing Emerging Vulnerabilities
Imagine starting your workday to find all your company’s sensitive data has been leaked due to a nearly imperceptible hack embedded in an AI prompt. The culprit isn’t the AI model but rather a lack of protections surrounding it. When rapidly adopting AI at scale, many organizations unintentionally ignore a crucial element: security and governance. Often, companies will only add operational AI rules post-deployment. However, this approach creates hidden blind spots and slows security teams’ responses when threats inevitably appear. In this presentation, Mark Toler will reveal why AI...
The Context Economy: Getting More Bang for Your Token
Building effective agents requires fundamental changes to how engineers think about their work. LLMs that sit at the heart of agentic systems are stochastic by nature and operate in a world that requires optimal context to produce optimal results. While models offer bigger and bigger context windows, the cost and performance during inference is tied to the consistency of context delivery and the stability of that context over time. As tool calling and the Model Context Protocol (MCP) ecosystem play a larger role in agentic development, engineers need to invest in not only delivering the...
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...
Agentic AI at the Edge: Building Mobile AI Agents for Automotive & MedTech
Shipping AI into regulated, safety-critical mobile apps is more than “just call an LLM.” In automotive and medical contexts, we must meet strict privacy, latency, and auditability requirements while working with flaky connectivity and device constraints. In this talk, Ronak will share a field-tested architecture for agentic AI on the edge: a hybrid edge–cloud agent that uses tool-calling plugins, a policy engine for pre/post-prompt guardrails, offline fallbacks, and structured telemetry for audits. He will walk through how his team bounds agent behavior with capability cards, redacts PHI/...
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...
Energy-Efficient AI: Building Sustainable Data Pipelines for the Future
Artificial Intelligence is driving innovation across industries, but its growing energy demands pose critical challenges around cost, scalability, and sustainability. In this talk, Bhanu will share practical strategies for designing energy-efficient AI systems, focusing on: dynamic batching & KV caching for reducing inference overhead, sparse neural networks & structured pruning for lightweight models, carbon-aware scheduling to align compute with renewable energy, federated learning & edge deployments to reduce data transfer energy, and a sustainability maturity model for...
Why Your Agentic PR Never Gets Approved—Going from Vibe Coding to Agentic Engineering
You can write 100% of your code with AI, and only be 15% more productive. Want to know why? Solving the problem of moving faster as an engineer is about way more than code. You design, you problem solve, you discuss and communicate, you test, you shepherd the PR through review, you deploy, and you observe. So why are you being told you should be 10x faster now that AI can write code? David will discuss practical solutions for safely accelerating your entire workflow with AI. He will look at how to collaborate with PM, how to use AI planning, how to ensure validations are solid and builds...
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...
Speed Without Fear: Controlling AI-Driven Releases
Learn how to move faster and safer with your AI projects. Whether you are using AI to help create applications with Github Co-pilot, Cursor, one of the other expanding code generation AI tools, or you are developing agents and utilizing various LLMs to support them, using feature flags can provide the ability to test multiple variations in production while providing a safety net that allows you to quickly resolve issues in production. AI is moving fast and the number of options available to you is increasing daily. Join the session to learn how you can validate and experiment with various...
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...
From Prompt to Production: Building AI Systems That Actually Work
AI can now generate code, tests, documentation, and even entire applications in minutes. Yet many organizations are discovering that faster code generation has not translated into faster delivery, higher quality, or greater business impact. Instead, teams are often facing more code churn, more technical debt, and more production incidents. The challenge is no longer getting AI to write code. The challenge is building systems that consistently deliver value to end users. Drawing on real-world experience building AI products, agent-based systems, evaluation frameworks, and production...
Beyond the Chatbot: Building and Orchestrating Autonomous Security Agents
The next generation of security automation isn’t found in a chat interface—it’s found in Agents. While basic AI assistants can provide security advice and even remediate vulnerabilities, the real breakthrough for security teams lies in using an Agentic Workflow for security testing: the ability to build, customize, and orchestrate specialized agents that take ownership of the testing and remediation lifecycle. In this session, Jeffery Payne demonstrates how to move beyond conversational AI to create autonomous security agents using standardized formats compatible across tools like GitHub...
User-centricity for AI-assisted Test Engineers
Traditional software testing is fundamentally deterministic: the same inputs must always produce the same outputs. Yet many teams introduce AI into their testing without first defining the problem the AI is meant to solve, leading to brute-force experimentation and unreliable results. Google’s 2025 DORA report highlights that user-centricity is a prerequisite for AI success and that AI is most effective when it is pointed at a clear problem. AP shows you how that insight applies technically to testing. Before AI can be used as a testing tool, it must first be tested and understood in the...