Prosus has shipped over 7949 agents. 15% have worked. The rest have been learning experiences. Let's talk about what we have learned, and where we see things going.

In this session, Ricky Doar, VP of Solutions at Cursor, shares actionable insights from leading large-scale AI developer tool implementations at the world’s top enterprises. Drawing on field experience with organizations at the forefront of transformation, Ricky highlights key best practices, observed power-user patterns, and deployment strategies that maximize value and ensure smooth rollout. Learn what distinguishes high-performing teams, how tailored onboarding accelerates adoption, and which support resources matter most for driving enterprise-wide success.

2025 has truly been and still is the year of agents. We’ll take a pragmatic look at what's happened and what we learned along the way. Then we'll map the next wave. We’ll distill product patterns behind the wins and preview what’s coming next: from capable agents, to out-of-the-box AI building blocks, and how models like GPT-5 simplify shipping novel, long tail user experiences.

The next generation of AI agents won’t just respond to what we say—they will sense how we feel. As large language model–powered agents move from research prototypes into production, a critical frontier is the integration of multi-modal affective computing: combining voice, text, facial expressions, and interaction patterns to detect the learner’s or user’s emotional state in real time.

This talk explores the challenges and opportunities of deploying emotion-aware AI tutors in production environments. Drawing from ongoing research at MIT Media Lab and Harvard, and from startup experience building GlowingStar, I will share how multi-modal signals—speech tone, facial micro-expressions, response latency, and even silence—can be fused into affective state estimates that meaningfully improve user experience.

We will unpack the technical lessons learned from moving affective sensing beyond the lab: designing architectures that combine ensemble LLMs with sensor inputs, diagnosing when modalities conflict or sabotage each other, and establishing guardrails for privacy and consent in sensitive domains like education. In parallel, I will highlight multi-agent orchestration patterns—including critic–rewriter loops and role-based ensembles—that make it possible to personalize instruction, generate equitable feedback, and sustain engagement across diverse learners.

By the end of this session, attendees will have a clear picture of what it takes to move multi-modal, affect-sensing agents from demos to durable production systems: the architectures, the pitfalls, and the metrics that matter. More importantly, we will consider how these lessons extend beyond education to any industry where AI agents must not only think, but also feel with and for the human in the loop.

Personalization at scale needs deep understanding of each customer. You must collect data from many sources, read it, reason and infer, plan, decide, act, and write to each person. One agent doing everything gave us poor and inconsistent quality. Multi-agent systems changed that. They deliver mass personalization. They also break in edge cases, contradict each other, and are hard to debug.

I will share how we addressed this with Cortex UCM, a unified customer memory, and Generative Tables. We map noisy data into a clean, structured layer that agents read and write. We began with email for both outbound and inbound communication. Then we personalized websites and product pages for e-commerce at scale. I share customer stories. For example, one customer had over 60,000 product pages that required customization for thousands of communities and product offerings.

I will present our decentralized shared-memory orchestration briefly and how it stays transparent and debuggable. It opens safe paths for external agents. What failed. What worked. What we are building next.

In this session, we’ll explore how developing and deploying AI-driven agents demands a fundamentally new testing paradigm—and how scalable simulations deliver the reliability, safety and human-feel that production-grade agents require.

You’ll learn how simulations allow you to:

• Mirror messy real-world user behavior (multiple languages, emotional states, background noise) rather than scripting narrow “happy-path” dialogues.
• Model full conversation stacks including voice: turn-taking, background noise, accents, and latency – not just text messages.
• Embed automated simulation suites into your CI/CD pipeline so that every change to your agent is validated before going live.
• Assess multiple dimensions of agent performance—goal completion, brand-compliance, empathy, edge-case handling—and continuously guard against regressions.
• Scale from “works in demo” to “works for every customer scenario” and maintain quality as your agent grows in tasks, languages or domains.

Whether you’re building chat, voice, or multi-modal agents, you’ll walk away with actionable strategies for incorporating simulations into your workflow—improving reliability, reducing surprises in production, and enabling your agent to behave as thoughtfully and consistently as a human teammate.

Live improvised music with lyrical suggestions from the chat

Developing AI agents for shopping is just the first step; the real challenge is reliably running them in production across complex, mission-critical e-commerce systems—a significant MLOps hurdle.

In this talk, we'll talk about Alfred, our agentic orchestration layer. Built with tools like Langgraph, LangFuse, LiteLLM, and Google Cloud components, Alfred is the critical piece that coordinates LLMs with our entire e-commerce backend—from search and recommendations to cart management. It handles the complete execution graph, secured tool calling, and prompt workflow.

We’ll share our journey in designing a reusable agent architecture that scales across all our digital properties. We’ll discuss the specifics of our tech stack and productionization methodology, including how we leveraged the MCP framework and our existing platform APIs to accelerate development of Alfred.

This presentation discusses the evolution of AI agent interaction, focusing on transitioning from low-engagement text-based chat to more intuitive, GUI-driven experiences. It outlines critical challenges in creating an intuitive and impactful experience for busy dealers, proposing solutions that include quick actions, efficient data streaming, and agent interactivity to create a great user experience.

The rapid spread of digital misinformation requires solutions that address the entire lifecycle, moving beyond single-LLM limitations. This talk, based on the author’s ICWSM research paper, offers a practitioner's guide to a novel, five-agent system—Classifier, Indexer, Extractor, Corrector, and Verification—designed for maximum scalability, modularity, and explainability. This paper aims at automating the working of fact-checkers, which is traditionally done through a team of experts, saving millions and increasing efficiency with a human-in-the-loop system. We will get the details for each specialized agent, detailing crucial elements like model sizing and fine-tuning—for example, matching small, fine-tuned encoder models for the Classifier's high-confidence multi-class labeling against the need for a strong reasoning LLM in the Corrector Agent. Topics include building an efficient Indexer Agent and reranking with retrieval through hybrid keyword and vector embeddings, enabling the Corrector Agent to use external search APIs for cross-validation, and the function of the Verification Agent as the final quality check for high precision.. The talk concludes by covering agent coordination protocols, cost, holistic evaluation, offline evaluation and online A/B testing and post-deployment metrics.

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Single-agent LLM systems fail silently in production - they're confidently wrong at scale with no mechanism for self-correction. We've deployed a multi-agent orchestration pattern called ""structured dissent"" where believer, skeptic, and neutral agents debate decisions before consensus. This isn't theoretical - we'll show production deployment patterns, cost/performance tradeoffs, and measurable reliability improvements. You'll learn when multi-agent architectures justify the overhead, how to orchestrate adversarial agents effectively, and operational patterns for monitoring agent reasoning quality in production.

Our first deployment of the debate swarm revolves around MCP servers - we use a security swarm specially built for MCP servers to analyze findings from open source security tools. This provides more nuanced reasoning and gives a confidence score to evaluate the security of unknown MCP tools.

The discussion centers on highly technical yet practical themes, such as the use of advanced post-training techniques like Direct Preference Optimization (DPO) and Parameter-Efficient Fine-Tuning (PEFT) to ensure LLMs maintain stability while specializing for e-commerce domains.

We compare the implementation challenges of Computer-Using Agents in automating legacy enterprise systems versus the stability issues faced by conversational agents when inputs become unpredictable in production.

We will analyze the role of cloud infrastructure in supporting the continuous, iterative training loops required by Reinforcement Learning-based agents for e-commerce!

This talk covers an overview of AI coding tools and different levels of coding automation. It also discusses workflow patterns that have emerged and how they will change over time.