The rapid evolution of AI agents is exposing a widening gap between their unique computational needs and today’s infrastructure. This keynote cuts through the hype to highlight why traditional compute paradigms—mainframes, VMs, containers, even serverless—are struggling to keep up with agents’ bursty, stateful, and hardware-hungry workloads. We’ll examine the economic and technical inefficiencies organizations face, from unpredictable scaling to persistent state management, and why simply “tweaking the cloud” won’t cut it. Expect a candid look at the real operational challenges, the architectural dead-ends, and the tough question: do we adapt existing frameworks, or is it time for a radical rethink of how we design and manage compute for the AI era? Actionable insights, not wishful thinking.

As AI continues to evolve, we will a shift from static chatbots to dynamic agentic AI systems capable of autonomous reasoning, tool integration, and multi-step problem-solving. This talk explores how to design AI agents that leverage structured outputs, function calling, and workflow orchestration with Google Gemini.

We’ve all built tools that looked good in demos but broke the moment we let go of the wheel. This talk is about building something different—systems that don’t just run, but run well when no one’s watching.

In under 10 minutes, I’ll walk through how I designed an execution layer that handles thousands of operations daily—without melting under pressure, without drifting off course, and without needing constant supervision.

I’ll share the real structure: • How it thinks • How it decides what to do next • How it knows when to stop • And how it stays sane when things go wrong

No theory, no fluff—just what it took to make something dependable in a world that isn’t.

Every engineer building AI agents has experienced it: you tweak a prompt, swap out a model, or adjust a RAG setting—only to find it either worsens the agent or improves one aspect while breaking another. Why does this happen? Because teams typically test just one configuration out of countless possible combinations, hoping for the best.

Current evaluation tools are built for single-point assessments, not the extensive multi-dimensional comparisons that real-world scenarios demand. Sure, you might be able to A/B test two prompts or select from a few models, but exploring hundreds of configurations across dimensions like cost, latency, and accuracy simultaneously is nearly impossible.

In this talk, we'll demonstrate how adopting a structured approach to testing alternatives can significantly change outcomes. Leveraging concepts from multi-objective optimization, we’ll illustrate how Traigent's SDK and UI empower engineers to allocate their testing budgets effectively. Traigent intelligently identifies and explores promising configurations, highlighting optimal tradeoffs. You'll learn how this methodology can yield quality improvements of 4–7x and reduce costs by up to 90%, all without resorting to guesswork or manual trial-and-error.

Underwriting Assist is a LangChain- and Ray-powered multi-agent system that accelerates insurance underwriting by 3x and cuts manual errors by 40%. It leverages RAG, shared memory, and LLM-based agents for clause analysis, risk profiling, and rationale generation. Real-time evals and human-in-loop feedback ensure accuracy, explainability, and regulatory compliance at scale.

Healthcare is one of the most vital and far-reaching sectors in our society, touching every individual at some point in their lives. Yet, it faces mounting challenges: rising administrative burdens, increasingly complex disease patterns, and growing patient volumes strain already stretched systems. In this talk, Lars will explore the untapped potential of AI agents to address some of healthcare’s most pressing real-world problems. He will present Corti’s unique approach to developing domain-specific agents equipped with healthcare-relevant skills—engineered not only for impact, but within a framework that places governance and safety at its core. Join us to learn how AI can be responsibly and powerfully deployed to support the future of care.

As AI agents become active participants in production environments, handling infrastructure tasks, chaining tools, generating outputs, and executing plans across cloud services, the financial implications are often underestimated. These agents may appear intelligent, but they have zero awareness of cost boundaries. A single agent loop with poorly bounded retries, excessive API calls, or unrestricted tool usage can quietly rack up hundreds or even thousands of compute, token, or storage costs.

In this session, I’ll walk through how seemingly harmless design decisions, like overly verbose prompts, excessive tool chaining, or unrestricted LLM usage, can result in runaway spending. I’ll share lessons from deploying agentic systems in cloud-native pipelines and infrastructure security tools, including my work on DockSec, an open-source AI-powered container security analyzer. We’ll explore how agents misbehave in cloud billing terms and what the attendees can do to stop it.

Attendees will learn practical strategies to monitor, contain, and optimize agent costs: from integrating cost observability into your agent stack, to programmatically setting retry, token, and API call budgets, to leveraging agent memory, caching, and behavior throttling to reduce waste. Whether they’re scaling agents in production or just starting to build them, this talk will give them the tools to design agent systems that are not only intelligent but also financially sustainable.

The rapid spread of digital misinformation requires solutions that move beyond simple detection and address the entire lifecycle of false narratives. This talk will first introduce the fundamental concepts of Multi-Agent Systems (MAS), exploring their core components, architectural patterns, and the collaborative potential of specialized AI agents. We will cover how these systems are designed to break down complex problems into manageable tasks, fostering modularity, scalability, and enhanced performance through cooperative and competitive agent interactions. Building on this foundation, the second half of the talk will transition from theory to a practical application, detailing a novel multi-agent framework designed to manage the complete misinformation lifecycle. As presented in our recent paper, this system employs a pipeline of specialized agents for the classification of misinformation types, evidence-based detection, automated correction, and source identification. We will walk through the architecture of this system, demonstrating how each agent contributes to a more transparent, reliable, and holistic approach to combating misinformation at scale. This talk will provide attendees with a deeper understanding of how multi-agent systems can be practically implemented to address one of the most significant challenges in our modern information ecosystem.

Agent memory and organizational knowledge are actually one in the same. Organizations remember the whys, and how-tos by writing down learnings in a centralize place. For agents to remember, they need to do to the same.

But they both suffer the same shortcoming - it's impossible to keep up-to-date. Can we solve both problems at once?

If your goal is to accelerate development of agentic systems without sacrificing production quality, a great choice is to use simple, composable GenAI patterns and off-the-shelf tools for monitoring, logging, and a few other capabilities. In this talk, I'll present an architecture consisting of such patterns that will enable you to build agentic systems in a way that does not lock you into any LLM, cloud, or agent framework. The patterns I talk about are from my GenAI design patterns book which is in early release on O'Reilly's platform.

As we integrate agentic AI into business products, robust evaluation of the agents is essential to delivering the highest quality. Proper evaluation ensures that AI agents are reliable, safe, effective, and aligned with user intent. Unlike traditional software or machine learning models, AI agents are non-deterministic and require specific types of evaluation. This talk outlines the importance of evaluating AI agents, the key components that we version and test at Acre Security, the metrics that matter for different types of agents, and how we currently achieve success evaluating AI agents that we build at Acre.

it’s 2 a.m., and a critical service slows down. Dashboards scream red—packet loss, timeouts, delays. The clock is ticking. Eyes race across a maze of graphs, flipping through visualizations and route tables. One graph leads to another. A dozen tabs open. Fatigue sets in. You’re left guessing: Is it the network, the application, or something else? Welcome to the new normal in network operations—where telemetry is endless, but clarity is rare. This session explores how AI and large language models (LLMs) transform observability by evolving views from data presentation to intelligent data interpretation. Instead of manually piecing together clues, imagine asking, “What’s wrong here?” and receiving clear, contextual insights. AI-powered storytelling augments human reasoning, reduces noise, and accelerates fault isolation—lowering misdiagnosis risk and improving mean time to identify (MTTI) and resolve (MTTR). Join us to see how storytelling is reshaping digital operations.

AI agents aren't just generating content; they're generating traffic. Like any good agent, your AI agent isn’t working alone. Behind the scenes is a mission-critical handler: the AI Gateway.

In this lightning talk, we'll explore how Gateways are evolving to handle the evolving realities of GenAI: dynamic routing, access control, cost-aware load balancing, model-aware failover, and observability across multi-model environments.

If you're building agents or just trying to keep up with the traffic they generate, this talk will help you understand the infrastructure patterns that are evolving to support a new landscape of software.

This talk shares the ongoing, real-world journey of building agentic infrastructure at AlwaysCool.ai—from simple GPT-based tools to our first production-ready AI microservices. We started with small wins like automating nutritional analysis and FDA label validation, but quickly ran into issues with sync limits, cost control, and debugging blind spots.

That led us to build a shared agentic service layer, using LangGraph to orchestrate multi-step flows and FastAPI to serve those agents cleanly. With OpenTelemetry at the core, we now send metrics and traces to Prometheus, Grafana, and LangSmith for real-time visibility, which is critical for compliance workflows such as HACCP, CAPA, and FDA traceability.

We’re not claiming to have it all figured out—this is a story of learning in the open, much like we do at the Austin AI Middleware Users Group (AIMUG). If you're navigating the same terrain—tooling decisions, observability gaps, or production pressure—this talk offers patterns, tools, and cautionary lessons worth carrying into your own journey.

Quality is the top barrier preventing Agentic applications from reaching production. This talk introduces Evaluation-Driven Development, a methodology that uses evaluation as the cornerstone for building high-quality, reliable Agentic systems. We will demonstrate how to drive it with MLflow 3.0, a new generation of the popular MLOps platform redesigned for the LLM era, including one-line observability, automatic evaluation, human-in-the-loop feedback loops, and monitoring.