AI agents are rapidly moving from demos to production, acting autonomously across tools, data systems, and workflows—and in the process, they amplify data movement far beyond what traditional governance models were designed to handle. Data security controls built for humans break down when agents operate at machine speed, execute in parallel, and persist sensitive information across new data surfaces like trajectories, embeddings, logs, and tool outputs.

In this talk, we outline the emerging data governance failures in agentic architectures—identity confusion for data access, entitlement creep, recursive leakage across agent chains, new data constructs leading to old controls becoming obsolete, and why out of box agent harnesses and existing IAM are insufficient. We then present Meta’s governance-first approach for safely enabling agents at scale: a defense-in-depth stack centered on Isolation Domains (domain-scoped encryption and output closure), Agent Identity (end-to-end attribution distinct from the user), Agent-Aware Access Control (classification-aware ABAC evaluated at query time), AccessMate (zero-standing-permissions access triage and least-privilege fallback), CodeGuard (secure code generation and runtime execution guardrails), and DataVM—a unified trusted data environment that bounds inputs, tools, and outputs under one governed scope.

Attendees will leave with a concrete reference architecture for building agents that are not merely powerful, but governable, auditable, and regulatory-ready—turning governance from a blocker into the harness that safely unlocks agent autonomy.

The excitement around agentic AI is real — backed by quantitative progress on model cards and genuine leaps in capability. But our ability to measure AI has been outpaced by our ability to develop it, and closing this evaluation gap is one of the most important problems facing the field. More enduring benchmarks are needed to advance the next vectors of capability and chart the path to reliable agents.

In this talk, Snorkel AI Co-Founder and CEO Alex Ratner will share insights from major research and benchmark collaborations on agentic coding and continual learning, along with practical tips from working with global frontier labs and leading academics. He'll focus on three dimensions where today's models most often break down, and where the next generation of benchmarks will need to deliver real signal: environment complexity (how dynamic and rich the operating world is), autonomy horizon (how far an agent can act independently), and output complexity (how sophisticated and verifiable the deliverable is).

The security community spent decades building rules and frameworks that made systems harder to break. AI has fundamentally upended those lessons -- attackers are now more enabled than ever, and traditional defences don't translate. This talk examines prompt injections, indirect prompt injections, and jailbreaks, showing why each resists simple fixes. Drawing on hands-on experience building AI security tools, I'll demonstrate why rules-based approaches fail against systems that interpret natural language as instruction. But there is hope: I'll share defensive approaches that actually work and outline a credible path toward resilient AI systems.

Users have long wanted to understand and control the algorithms that shape their recommendations, but enabling meaningful user agency over recommendations has remained challenginging — until now. We present Tune-Your-Algorithm (TYA), an AI-powered agentic recommendation system on Instagram that gives users transparent visibility into the recommendation algorithm and the ability to tune it using natural languages.

TYA is built on two key innovations: (1) the MRS Memory System (Biography), an LLM-based framework that summarizes user engagement histories into rich, structured, and interpretable user interest and intent representations at scale; and (2) Think-Then-Recommend (TTR), a reasoning-augmented approach that decomposes user interests and complex user intents into personalized sub-goals for personalized and contextualized recommendations.

Early results show strong product-market fit with positive user feedback on transparency and user agency enabled by TYA. We discuss the end-to-end architecture, production learnings, technical challenges we are actively tackling, and the path towards our north star vision.

As the industry pivots toward an "AI Native" paradigm, the bottleneck for innovation has shifted from algorithmic design to the underlying infrastructure's ability to handle unprecedented scale and complexity. This session explores how Google TPU (Tensor Processing Unit) infrastructure serves as the catalyst for this transformation, specifically within the domains of large-scale Recommender Systems, MoEs, LLMs and the emerging era of Autonomous Agents.We will delve into the architectural innovations of the latest TPU generations, demonstrating how their purpose-built design facilitates the massive throughput required for real-time recommendation engines and the high-speed inference necessary for agentic orchestration.

Training data for Meta's recommendation systems was entirely stored in Data Warehouse, structured as relational tables where each row captures labels and snapshotted features at the point of recommendation.

New modeling techniques, such as learning from user sequences and multi-modality, has led to a 10-100x increase in feature size, making the training data increasingly cost-prohibitive due to high duplication. The same user's features are stored repeatedly for every recommendation request, with highly popular content features being duplicated potentially over a million times.

We present a co-designed data and infrastructure in order to address the scaling challenge. By moving features out of training samples into a high-performance indexing storage and implementing model access pattern-aware pushdown optimizations, we have achieved a 10x storage cost reduction for the largest feature: long user sequences.

Every major business decision is ultimately built on data, yet getting to the right answers has long required specialized expertise, from knowing which tables to query to how to query them to building the right data applications. AI agents are fundamentally changing that equation, making data accessible to anyone who can ask a question in plain language. At Meta's scale, with millions of datasets serving tens of thousands of decision-makers, this shift creates both massive opportunity and unique challenges around trust and accuracy. This presentation will detail how we built AI-native data experiences to address two key dimensions: enabling trusted answers through agentic data consumption, and letting users create shareable agentic data applications without writing a single query.