That's a wrap!

It started with a simple HTML artifact. Now it's becoming something else entirely. In this session, you’ll see how artifacts at Glean have evolved from static outputs into dynamic, interactive experiences that empower anyone to become a builder. We’ll walk through how teams are using interactive artifacts to visualize metrics, create shareable content, build presentations, generate reports, and more, all from a conversation. You’ll leave with a clear understanding of the patterns behind these experiences and how to start creating them for your own use cases.

Permissions-aware means secure. Read-only agents are low risk. Audit logs are enough. Governance slows adoption. You've heard these takes in every meeting, every Slack thread, every vendor pitch. But are they true? In this fast-paced session, we'll take on the most common security assumptions about AI agents and put them to the test. For each myth, we'll walk through a real scenario, show you where the assumption holds up or falls apart, and land on the practical takeaway. This isn't about fear. It's about replacing gut feelings with evidence so your team can move fast without fooling itself about what's actually safe.

Enterprise AI adoption is evolving through three stages: assistants that make company knowledge more self-service, co-builders that help employees create solutions and automate individual tasks, and agents that orchestrate end-to-end workflows. In this panel, leaders will share how they see this progression unfolding in their organizations and across multiple industries, where they are focusing now, and what is required to move from one stage to the next. The conversation will explore the opportunities and challenges of moving beyond chat-based AI: making governed enterprise knowledge and data accessible, enabling employees to build solutions more quickly, and establishing the security, governance, and human oversight needed for trusted workflow automation. Panelists will also discuss how Snowflake, together with partners such as Glean, can connect secure enterprise data, knowledge, and business workflows to support this progression. Ultimately, this discussion will deliver clear, cross-industry perspectives on turning early AI wins into enterprise-wide impact.

Every vendor wants to be the front door to AI for your employees, whether that’s the IDE, chat app, your legacy productivity suite, or the newest AI on the market. But in a market defined by constant innovation, the choices you make today may not be best-in-class tomorrow. At the same time, you need your systems to work together and your AI strategy to stay coherent, sustainable, and scalable.This session maps the landscape, explores which tools are likely to win, and outlines how to build a scalable long-term approach. You’ll leave with a clearer understanding of the tradeoffs behind different AI front door approaches and a defensible point of view on where to standardize and where to stay flexible.

A strong AI use case is not yet a strong solution. Teams often know the pain point they want to solve, but get stuck translating that idea into the right combination of Glean primitives, agent patterns, actions, workflows, and implementation choices. In this session, we'll walk through a practical methodology for moving from a customer or internal use case to a well-designed solution on Glean. We'll show how Glean teams evaluate the job to be done, identify the real user workflow, decide what should be retrieval vs. reasoning vs. action, and choose the right build surface for the problem. Along the way, we'll share the tools, design artifacts, and working practices that help teams move faster and avoid common dead ends. This session complements ADLC, but focuses on the first 3 phases in the process: before you are scaling agents, how do you frame the problem correctly and design a solution that is actually worth building?

Glean's ability to deliver personalized, proactive experiences starts with understanding your organization, its people, content, and workflows. This session dives into the Enterprise Context layer, the foundation that includes Glean's enterprise and personal graphs and indexing capabilities. We'll walk through the latest advancements in how Glean captures and models organizational knowledge, workflow logic, and individual work patterns, and show you how that context enables Glean to surface the right information and actions at the right moment, often before users even have to ask.

Most companies do not have an AI problem. They have a sprawl problem. In this session, Brandon Berg will share how Infoblox used Glean as a common foundation for enterprise AI, giving teams one place to build instead of chasing disconnected tools and one-off solutions across the business. He'll walk through Infoblox's next challenge: moving from a tightly managed agent environment to a more open model that still preserves governance, visibility, and quality. The session will cover what changes when leaders stop asking whether people are using AI and start asking whether teams are duplicating work, building the same thing in parallel, and wasting time and tokens reinventing agents that already exist. Brandon will also make the case for a more practical enablement model: less obsession with formal champions, more focus on listeners who can surface what is actually working across the organization and help operationalize it at scale. Attendees will leave with a clearer view of what AI maturity looks like after the pilot phase, and how to balance openness, standardization, and token efficiency as adoption scales.

Howard Hughes is one of the largest master-planned community developers in the U.S., with a portfolio spanning millions of square feet of commercial and residential assets. In 2025, leadership set an aggressive, enterprise-wide mandate to fundamentally reshape its cost structure through AI-driven efficiency, with Glean at the center of that effort. In this session, we share how Howard Hughes got there: starting with a focused 100-user pilot, building early wins around core knowledge management and workflow automation, and earning a mandate to deploy Glean wall-to-wall across the entire company. You'll hear what use cases drove early credibility, how the team scaled from pilot to enterprise rollout, and what it took to build the internal conviction to go all in, including the agents, integrations, and operational changes that separate a pilot that stays a pilot from an AI deployment that actually ships.

The AI ecosystem is converging on a set of open standards and primitives faster than most engineering teams can keep up. Model Context Protocol (MCP) is becoming the universal tool interop layer, while frameworks like Agent-to-Agent (A2A), Agent-to-UI (A2UI), Agent Development Kits (ADKs), and Cross App Access (XAA) are redefining how tools, skills, and agentic workflows interact across platforms. In this fireside chat, ecosystem partners dig past the hype and into the technical architectures actually taking shape. Expect an engineering-level breakdown of how these emerging standards are being built, adopted, and wired together across the modern AI stack.

Frontier and open models: crafting a sustainable enterprise strategy. Choosing a model used to be a single, binary decision. Today, it's a dynamic portfolio strategy and enterprises are wrestling with knowing how to implement it at scale. Frontier models continue to push the boundaries of complex reasoning, while open and small, specialized models are closing the quality gap at a fraction of the cost. Navigating this hybrid reality has made model strategy a topic that lands directly on the CFO's desk, requiring a shift toward economics, orchestration, and long-term sustainability. Join us for a discussion on why frontier and open models aren't in a zero-sum battle, but rather complementary engines in the enterprise stack. We'll explore why metrics like "token yield" are rising in importance, how lower-cost models alter workload design, and where durable competitive advantage actually lives -- whether in the weights themselves or in the context, harness, and orchestration surrounding them.

What does it actually take to turn an AI pilot into real business momentum without getting stuck in pilot purgatory? In this session, we'll share a practical approach for moving from early experimentation to measurable impact, including how to choose the right use cases, assign clear owners, define proof of value, and build the internal credibility needed to scale. We'll also cover how leaders should sequence the fundamentals like workflows, governance, literacy, and platform readiness, what to prioritize first, and how to set explicit kill-or-scale gates early so teams can make confident decisions and create a repeatable path to transformation. Learn from Tim Glomb, a 20+ year operator, marketer, and AI transformation leader who specializes in transforming how businesses work.

Anyone can get employees access to AI tools. Far fewer know how to transform access into lasting habits, real workflow change, and better judgment over time. In this session, Canva shares how its AI strategy matured from early rollout and employee enablement into a more disciplined model for scaled adoption. You'll hear how Canva expanded beyond a single-tool approach, built an internal adoption system around access and learning, supported different types of users as needs became more sophisticated, and began shifting from "AI everywhere" to using AI in the right places for the right reasons. The session will also explore a harder question many leaders are just beginning to face: how do you scale AI use without weakening critical thinking, work quality, or trust?

We're getting out of the way for this one. There's a ton of shared knowledge in this room, and this session is designed to put it to use. After a quick round of introductions, you'll break into small group discussions around the topics that come up in every admin's life: getting new admins onboarded and up to speed, building a playbook for connector rollouts, monitoring platform health, tracking usage and adoption, figuring out which metrics actually matter, balancing AI enablement with governance, managing AI costs, and getting employees bought in. No slides, no demos, just honest conversation with the people doing the same work you are.

What's possible when you push Glean's agent-building capabilities to the limits? Learn from Glean's experts to understand the bleeding edge of what's possible, leveraging advanced agent capabilities to create powerful AI coworkers.

Bring your most painful Support workflow and leave with a clear plan to improve it. In this interactive session, Vlad Tsapko from Insurity will share how his team is applying Glean to real-world Support and Operations challenges, followed by a Glean speaker and demo. You will learn how Glean connects context, reduces repetitive work, and drives Support workflows. You will then work with peers and Glean experts to map one high-friction workflow, define the problem and desired outcome, and identify the right path forward - an agent, skill, or concrete blueprint.

AI is a race for speed, and many vendors are prioritizing time to market above all else. In too many cases, security and governance come later -- if they come at all. Meanwhile, enterprises are being pressured to adopt these tools without fully understanding the risks, or are knowingly accepting them in order to unlock AI across the organization. This is a conversation about how to manage risk without slowing innovation, and about where enterprise security is headed in a world where vendor security is too often an afterthought.

What makes enterprise AI truly intelligent isn't just model quality. It's whether the system routes each task to the right model at the right reasoning level, avoids burning premium tokens for everyday tasks, and learns to repeat success without expensive re-exploration. This session explores how Glean is building that intelligence layer. We'll cover how a model-agnostic harness gives enterprises access to leading frontier models while using specialized, task-optimized models and intelligent routing to deliver frontier-level outcomes at a fraction of the token cost. From there, we'll show how enterprise intelligence compounds over time: skills that package your company's know-how into reusable playbooks, context that tailors AI to your work processes, and continuous learning that captures what works and makes it repeatable. The way you use Glean compounds, reflecting how your teams actually think and operate.

Knowing what the Glean platform can do is one thing. Seeing what other customers have built with it is another. In this session, Mark Jerome Cruz from LinkedIn and Pavan Ratnakar from NVIDIA present examples of what they built with Glean and share the experiences and technical decisions that shaped each implementation. Their stories show how teams use different platform surfaces, including APIs, MCP, Web SDKs, or client SDKs, to create experiences and products that fit their needs. The conversation will explore how each team balanced between what they built themselves and what Glean provided. It is a practical look at what builders can create with the Glean platform, how it can fit into an existing technology stack, and what other teams can learn when taking an idea from an initial build to production.

We'll cover what's new in the protection layer and show you how they work, how they're configured, and how they address the security requirements that matter most to enterprise teams. You'll leave with a clear understanding of what's possible today and a concrete view of what's coming next.

Join this session for a practical look at Coinbase's journey with Glean, from the pain points and early use cases that shaped the rollout to the philosophy, implementation approach, enablement strategy, and end-to-end architecture behind it. You'll hear how their teams are driving value across the full employee base, building custom solutions on top of Glean, and improving stickiness and retention across their customer base.

Getting real value from AI agents at work takes more than a few demos and a good prompt. It takes a repeatable way to design, build, and scale what actually works. In this breakout, we'll walk through the Agent Development Lifecycle (ADLC): a practical framework for creating agents that fit your business, deliver measurable impact, and avoid a mess of one-off experiments.

Databricks partnered with Glean to solve a familiar challenge: helping 10,000+ employees spend less time searching across fragmented systems and more time doing meaningful work. In this session, hear how that internal success story evolved into a strategic partnership and what it unlocks for joint customers. Databricks and Glean will demo two integration patterns: Genie in Glean Assistant, which lets users ask natural-language questions against governed data without leaving their familiar workflow, and Glean MCP in Agent Bricks, which enables developers to build AI agents that reason over enterprise knowledge with full context. Walk away with real use cases, practical adoption lessons, and a clear picture of how connecting enterprise knowledge to governed data delivers better AI outcomes faster.

AI success doesn't come from launching a pilot. It comes from deploying AI in ways employees actually trust, adopt, and use to drive measurable business outcomes. In this customer panel, leaders at General Mills, Nationwide, Marvell, and Everpure will share how they moved from early experimentation to real-world deployment, including how they chose the right use cases, addressed governance and security requirements, drove change management, and proved value after launch. Attendees will leave with practical lessons for turning AI from a promising pilot into a scaled, repeatable part of how work gets done.

How do you turn AI value into an ROI story that executives actually trust? In this breakout session, you'll learn how to define ROI in plain language for executives, finance, and business stakeholders, identify the KPIs that matter at every altitude -- from employee to team, department, and company -- and connect AI investments to clear baselines, realistic targets, and credible business narratives. We'll also explore how to balance spend, time-to-value, and long-term strategic capability building so you can move beyond vague productivity claims and make a compelling, practical case for AI that resonates with leadership.

Most AI talks are highlight reels. This one isn't. We're going to walk to the edge of what today's AI can actually do and show you exactly where and why it breaks down. Writing that sounds competent but says nothing. Analysis that looks rigorous but is quietly wrong. Long tasks where small errors snowball into nonsense. Understanding where AI fails isn't pessimism. It's one of the most valuable things you can know because it shows you where to keep a human in the loop, where to add verification, and where human judgment matters most. You'll leave with a clearer sense of AI's edges so you can train your employees on how to look for them and apply additional judgement when needed and build systems that are honest about their limits instead of confidently wrong.

Learn about all of the new developments in Glean Agents that will unlock productivity across teams, including new features for agent builders and admins. Through real departmental examples, you will see how Glean is making agents more powerful, more practical, and more ready for enterprise-wide use.

The best AI experience isn't the one that answers your question. It's the one that knows what you need before you ask. In this session, we'll dig into how Glean uses context to deliver proactive, personalized AI experiences, surfacing the right information, the right action, or the right nudge at the right moment. We'll walk through the technical foundations that make this possible: how we model what you're working on, what signals we use, and how we determine what's relevant right now vs. what's noise. When the system already understands your context, it can anticipate, prepare, and act. We'll show where this is today, where it's going, and what it means for how your organization works with AI.

In this hands-on workshop, you will learn how to analyze business data across both structured and unstructured sources. Using natural-language queries and a data analysis Skill, you will gather evidence, combine data from sources such as data warehouses, business applications, documents, and conversations, and turn your findings into an interactive dashboard. This session is open to all knowledge workers and no data or technical background is required. Bring your own laptop with access to Glean and leave with practical experience building, refining, and validating an analysis artifact for your own business question.

Modern work is buried in email threads, chat messages, one-off meeting notes, and fragmented follow-ups. In this demo workshop, attendees will learn a repeatable pattern for using Glean to summarize conversations, pull out action items, create clear next-steps, and collaborate with Glean to get work started -- without needing to ask. See how Glean reduces missed follow-ups, minimizes context switching, and helps knowledge workers move faster with more confidence. Attend this session to get more from your email and Slack threads to collaborate and drive action with AI, not just better organization.

Every knowledge worker has to report on their work. Weekly status updates, QBRs, team standups, executive snapshots -- the format changes, but the grind is the same: digging through Jira tickets, Slack threads, meeting notes, and email to reconstruct what actually happened. In this workshop, you will learn how to use Glean to collapse that process from an hour to minutes. We will show you a repeatable pattern for pulling context from across your calendar, project tools, and communications into a structured, accurate status update. Whether you are preparing a weekly team snippet, a QBR, or a quick executive pulse, you will leave with a workflow you can run the next time one is due and get time back every week after.

AI security doesn't have to be hard. In this hands-on workshop, you'll design and implement guardrails from the ground up. We'll walk through scoping an agent, identifying its real risk surface, and writing policies that match. Along the way, you'll pick up the best practices that matter most: how to define your organization's risk boundaries, how to test before you deploy, and how to maintain guardrails as your agents evolve. If your team is building AI agents, this is the session that closes the loop on security.

See Glean newest innovations that turn individual productivity into consistent impact. With Glean, all work is grounded in employee and company data, helping your employees make better decisions, move faster, and produce higher-quality deliverables with less rework. In this session, we will highlight and demo the features that help users stay ahead of their work, create stronger output more efficiently, and move tasks forward across the tools they already use.

AI spend is rising fast, but most enterprises still cannot answer the most important question: is that spend actually producing better outcomes? As AI shifts from simple question answering to multi-step agentic work, costs balloon for reasons that go well beyond model pricing alone: too much context, too many tool calls, too many reasoning loops, and too little discipline around using the right model for the right task. This session explores why enterprise AI economics are breaking down, and what leaders need to change to regain control. We will unpack tokenomics as both a consumption problem and a yield problem: not just how many tokens you use, but how much business value you create per token.

Bring your most painful Sales workflow and leave with a clear plan to improve it. In this interactive session, Rachel Rubin from DoubleVerify will share how her team is applying Glean to real-world Sales challenges, followed by a Glean speaker and demo.

Headlines about AI have swung between two extremes -- it is coming for your job, or it is here to augment it. In this session, leading experts will bring data and research that examines which roles are being reshaped rather than eliminated, and why the companies seeing the greatest gains are redesigning work instead of simply layering on new tools. We will also address the harder truths directly, because credibility with your workforce disappears the moment you pretend the tradeoffs are not real.

Glean is more than an app you log into. It is a platform you can build your whole company AI on. This session goes deep on the platform layer: the API, the SDKs, embedded experiences, the LLM gateway, and the MCP gateway. We will show what is available today, what has changed recently, and where each piece is headed.

Every employee needs the right data at the right time -- but keyword search cannot surface the context to make that possible, and most enterprise data stays massive, siloed, and hard to trust. Deloitte faced this problem at scale and set out to solve it by building a centralized Data Marketplace. In this session, Rohit Balasubramanian and Ajay Tripathi will share how Deloitte evolved that Marketplace from manual, keyword-only provisioning to a contextual search experience powered by Glean. Leave with a concrete blueprint for turning fragmented enterprise data into a foundation that both people and agents can actually use.

We will share what is coming to the connector and indexing pipeline that will dramatically shrink the gap between connecting an app and having searchable content available for your users. We will preview new connector configurations designed to give you more control, walk through what the updated setup experience looks like, share new tools and connectors for you to enable, and show you the health metrics that will help you track freshness and coverage with confidence.

Sometimes a use case sounds promising, the build starts, and then progress stalls. The agent is inconsistent. The workflow is too brittle. The action model is wrong. The retrieval is weak. Or the team realizes too late that the problem was framed incorrectly in the first place. In this session, we will break down what to do when a Glean solution is not working the way you expected. We will walk through the most common failure modes in solution design and implementation, then show the patterns that help teams get unstuck. Attendees will leave with a set of practical design patterns, anti-patterns, and troubleshooting approaches they can apply to real projects.

Every agent run leaves a trace. Which paths it took, where it spent tokens and got something useful back, what worked well enough to try again. Most teams treat that as log data. The more interesting question is what happens when you actually use it. Attendees will understand a framework for continual learning and how it gets applied to models, harnesses, and context. The goal from learning is to make AI more efficient, smarter, and staying in sync with the evolution of the enterprise.

Calling all builders! Two Glean engineers sit down for a conversation about the Glean platform as it exists today, the direction it is moving, and the practical questions that matter when you start building. The conversation walks through the platform primitives and design approach. We also discuss MCP as an access path that makes Glean available where builders already work, and how the platform is evolving toward full parity with what the core product can do. You will leave with a better understanding of what the platform can do for you today, what is coming next, and how to decide which build surface fits your problem.

Most enterprise AI deployments stop at the interface. McCarthy did not. After a hard-nosed evaluation of Copilot, ChatGPT, and other options, McCarthy made Glean the front door for AI across the company then pushed far beyond search into agents and technical workflows. In this session, Alex Elsayegh shares how McCarthy approached that next layer of implementation and how the team built agent patterns that could do more than answer questions.

We believe admins should not have to wait for support or sift through logs to troubleshoot an issue. You should be able to ask, get an answer, and act on it. We are building toward an experience that is proactive, one that surfaces recommendations, flags things that need attention, and helps you figure out the right configuration for your specific organization. Attendees to this session will learn how to best utilize the latest in Glean administrative features and get a glimpse at the roadmap for features that make managing Glean faster and easier.

We have all got them. The team that never logged in. The exec who does not need it. The power user who tried Glean once and ghosted. This session looks at the real reasons adoption stalls after launch and how admins can turn early interest into lasting habits. Attendees will leave with practical ways to diagnose what is slowing adoption in their organization and stronger frameworks for helping Glean become part of how people work every day.