AI Con USA 2026 - LLMs
Sunday, June 7
Fundamentals of AI—ICAgile Certification (ICP-FAI)
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...
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...
Build Your Own AI News Agent with Redis + LangGraph.js.
NewTired of sifting through endless news sources and missing the topics you care about? This hands-on workshop empowers you to build a personalized AI news assistant using Redis and LangGraph.js. You’ll learn to ingest articles from RSS feeds, summarize and analyze them, extract structured data, and generate embeddings—all stored in Redis for powerful hybrid (vector + structured) search. Your agent will provide a web interface for browsing and semantic search, and even chat with you about current events, retrieving relevant articles and metadata using RAG techniques. LangGraph.js orchestrates...
Tuesday, June 9
Prompt Engineering for Software Practitioners
With the sudden rise of ChatGPT and large language models (LLMs), practitioners are using these tools for all aspects of engineering. This includes leveraging LLMs for creating software artifacts such as requirements documents, source code, and tests; reviewing them for issues and making corrective suggestions, and analyzing or summarizing results or outcomes. However, if LLM's are not fed good prompts describing the task that the AI is supposed to perform, their responses can be inaccurate and unreliable. Join Tariq King as he teaches you how to craft high-quality AI prompts and...
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,...
Agentic AI: From Rules to Reasoning
NewAI agents have existed for decades, but generative AI has fundamentally changed what agents can do and how they are designed and built. Come and explore the evolution of AI agents across two major waves. First, learn the foundations of Agentic AI through agents built using rules, heuristics, and traditional machine learning, examining where these approaches excel and why they struggle with complexity, ambiguity, and scale. Then dive into the second wave of agents powered by generative AI and multimodal large language models. These modern agents can reason, plan, use tools, and interact...
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...
Tracing the Mind of the Machine: Observability for AI Agents
AI agents have evolved beyond LLM chatbots; they possess the ability to plan, reason, and act autonomously. However, as their autonomy increases, understanding how they make decisions becomes more challenging. Traditional methods of observability—such as metrics, logs, and traces—capture outcomes but do not reveal the underlying reasoning. This session will explore how AI Agent Observability can shed light on the decision-making process by collecting and analyzing agent traces. We will discuss emerging standards like the Model Context Protocol (MCP), which provides structured and shareable...
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...
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/...
Thursday, June 11
The Master Control Policy Server: A Model-Agnostic Architecture for Enterprise LLM Governance and Risk Mitigation
As Large Language Models (LLMs) transition from conversational tools to autonomous AI agents capable of high-stakes, multi-step actions, the reliance on generalized, default safety alignment from model providers has become insufficient and often hazardous. This session addresses the critical need for application-specific, custom guardrails necessitated by persistent jailbreak vulnerabilities, the high cost of achieving effective fine-tuning alignment, and the challenge of enforcing proprietary organizational compliance (e.g., contextual PII protection or specific regulatory mandates unique...
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...
Engineering AI Infrastructure for Efficient Inference at Scale
As AI models grow in complexity and scale, inference efficiency has emerged as a critical engineering challenge for enterprise deployment. Traditional infrastructure built for training workloads often fails to meet the latency, throughput, and cost demands of large-scale inference operations. In this session, Sandeep will be sharing practical insights from engineering AI infrastructure at Broadcom, focusing on the end-to-end optimization of compute, networking, and storage subsystems. The talk explores techniques such as dynamic workload placement, adaptive batching, model quantization,...