Good afternoon, and welcome to Box's second quarter fiscal 2027 earnings conference call. I'm Cynthia Hiponia, Vice President, Investor Relations. On the call today, we have Aaron Levie, Box Co-founder and CEO, and Dylan Smith, Box Co-founder and CFO. Following our prepared remarks, we will take your questions. Today's call is being webcast and will also be available for replay on our IR website. Supplemental slides are now available on the website. On this call, we will be making forward-looking statements, including our third quarter and full fiscal year 2027 financial guidance and our expectations regarding our financial performance for fiscal 2027 and future periods, including gross margins, operating margins, operating leverage, future profitability, net retention rates, remaining performance obligations, revenue and billings, and the impact of foreign currency exchange rates. Our expectations regarding the size of our market opportunity, including the growing opportunity driven by the increasing role of unstructured data and AI agents in the enterprise, our planned investments, future product offerings, go-to-market initiatives and growth strategies, the timing and market adoption of, and benefits from our new products, solutions, and pricing models. Our ability to address enterprise challenges, including enabling organizations to automate critical workflows and deliver value for our customers. The benefits from our deepening partnerships with leading AI labs, hyperscalers, and systems integrators, and our capital allocation strategies, including potential repurchase of our common stock and future share count reductions. These statements reflect our best judgment based on factors currently known to us, and actual events or results may differ materially. Please refer to our earnings press release filed today and the risk factors and documents that we file with the SEC, including our most recent quarterly report on Form 10-Q for information on risks and uncertainties that may cause actual results to differ materially from statements made on this earnings call. These forward-looking statements are being made as of today, August 25th, 2026, and we disclaim any obligation to update or revise them should they change or cease to be up to date. In addition, during today's call, we will discuss non-GAAP financial measures. These non-GAAP financial measures should be considered in addition to, and not as a substitute for, or in isolation from our GAAP results. You will find additional disclosures regarding these non-GAAP measures, including reconciliations with comparable GAAP results in our earnings press release and in the supplemental slides, which can be found on the investor relations page of our website. Unless otherwise indicated, all references to financial measures are on a non-GAAP basis. Finally, please see our earnings deck posted on our IR website for a more detailed look at our Q3 and full year 2027 guidance. Thank you. With that, let me turn the call over to Aaron. Thanks, Cynthia, and thank you all for joining the call today. Box delivered exceptional second quarter results, continuing the strong momentum we saw in Q1 and led by the rapid customer adoption of Enterprise Advanced. Second quarter revenue exceeded our guidance, growing 9% year-over-year or 11% in constant currency, and produced operating margins of 29%. We drove a net retention rate of 106%, ahead of our expectations of 105%, driven by both price per seat increases and seat expansion. Our Q2 billings growth of 17% year-over-year and RPO growth of 15% year-over-year reflect the success of our strategic investments in both go-to-market and product roadmap in delivering solutions to customers that address their most critical challenges in AI. Some examples of our Enterprise Advanced wins in the quarter included a leading multinational investment bank that upgraded from Enterprise Plus to Enterprise Advanced, transitioning its legacy file servers to the Box platform. This deployment will expand its license to a wall-to-wall agreement to deliver unstructured data insights across its global banking teams. Next, a major federal agency upgraded from Enterprise Plus to Enterprise Advanced with a 4x seat expansion to replace its legacy contract lifecycle management and collaboration platforms. In partnership with Salesforce, Box will power secure, cloud-based CLM and document management across key legal and research divisions, replacing multiple SaaS vendors. This agency-wide modernization is enabled by Box's FedRAMP High compliance, our secure identity verified e-signatures, and Enterprise Advanced capabilities. With record Q2 bookings, these wins and many others make it clear that our role in enabling enterprises to get the most out of their enterprise content and transform in the era of AI is becoming increasingly significant. During the second quarter, I spoke with many enterprise technology leaders who highlighted their primary goals and challenges in implementing AI. One of the most common topics is how enterprises can get the right context to AI agents in a secure and governed way, as well as tap into the full value of their unstructured data. To do this, enterprises need a secure platform that can connect all the intelligence and capabilities of AI models to enterprise content and workflows. The world's most advanced super intelligence is only as useful as the underlying enterprise knowledge and corporate information that it has access to. Instead of companies sitting on millions or hundreds of millions of files that they know very little about, with AI agents, they can now ask questions about this data, mine it all for intelligence, and automate nearly any workflow that involves this enterprise content. This is the intelligent content management platform that we are building. These technology leaders that I'm speaking with are also recognizing that as AI model capabilities advance rapidly across an expanding set of vendors like OpenAI, Google, Anthropic, Meta, xAI, NVIDIA, and more that enterprises will need a model neutral platform that connects their content and workflows to these models and agents securely. With AI costs continuing to rise, the ability to draw the right cost performance mix with any vendor becomes essential. Rather than migrating content and workflows into separate systems to unlock AI's benefits, our intelligent content management platform gives enterprises a single platform where they can swap models or agents on their content at any time securely. Box is at the center of the greatest transformation in how enterprises work, and we are continuing to drive our product and go-to-market strategies to take full advantage of this massive opportunity. Building on our product leadership, in the second quarter, we announced a range of new capabilities that help customers transform the value of their content with AI. We introduced new security capabilities designed to give organizations greater control over AI agents working with their enterprise content. With new agent guardrails, third-party agent activity oversight, prompt injection detection, agent classification-based access policies, and more, customers will be able to extend Box's enterprise-grade security controls to both Box agents and third-party agents such as Claude, ChatGPT, Gemini, and more. To support our headless initiatives, Box announced new MCP integrations with Anthropic's Claude for Legal, Databricks, Harvey, IBM's watsonx Orchestrate Agent Catalog, Notion custom agents, Slack's Slackbot, and xAI's Grok. Box partnered with Anthropic as a launch partner for Claude's new legal industry solutions using the Box MCP server as the secure governance layer for agentic legal work. New MCP tools now let Claude execute multi-step matter operations directly in Box, copying and uploading files, tagging metadata, and managing collaborator access, turning Claude from a QA chatbot into an active practice agent. All actions stay governed by the firm's existing Box permissions and ethical walls, avoiding the governance gap of moving sensitive client data into unsanctioned tools. Also, earlier this month, we announced the release of the Box MCP server for Databricks, now available in the Databricks marketplace. This integration lets data analysts, scientists, and engineers combine, connect, and query unstructured content from Box, including their contracts, clinical records, financial assets, and specifications alongside structured sources like CRM and ERP, all without duplicating data or moving it outside of Box's secure governance boundary. This unlocks use cases across industries from healthcare teams spotting care gaps by combining clinical records with referral and billing data, to financial services firms accessing borrow and covenant risk by joining loan documents with banking data. As we look further into the second half of FY 2027, we're continuing to drive significant innovation across our platform to help enterprises maximize the value of their content in the era of AI. Building on the momentum of Box Automate, Box Extract, and Box Apps, our platform is evolving into a premier agentic workflow automation system designed to streamline critical content processes like client onboarding, contract reviews, brand asset verification, supply chain automation, and thousands of other workflows in an enterprise. Additionally, we're advancing Box Extract to help power complex document extraction needs across a range of industries from financial services to life sciences. Our model neutral agentic harness ensures that customers can both improve the accuracy of this extraction and lower their costs by choosing exactly the right model they need for any document type. Box is also modernizing its core content management infrastructure with improvements in metadata management, large file support, and file system capabilities. We are paving the way for enterprises to retire legacy on-premises ECM systems and migrate their unstructured data to a secure cloud-native platform where it can be easily accessed by AI. In Q2, we continued to see more and more enterprises look to migrate off these legacy systems in favor of a much more modern AI-driven approach. At Box, we are also optimizing our developer ecosystem to support AI agents working with enterprise content at scale and introducing new tools and improvements such as enhanced Box MCP server support, deeper integrations with leading agents like Claude, ChatGPT, Copilot, and Salesforce Agentforce, and improved context retrieval APIs, which will allow developers to securely connect enterprise content to AI agents. We are focused on delivering the world's best headless experiences for working with enterprise content securely across any AI agent and monetizing this usage through our AI units and API volume. Finally, all of these innovations are anchored by Box's industry-leading security and compliance foundation. As we recently saw with the OpenAI Hugging Face incident, enterprises will increasingly need platforms that can securely protect their corporate data and ensure that neither humans nor agents can get access to information they shouldn't have access to. As external AI agents interact with enterprise data, Box is implementing robust guardrails, comprehensive audit logs, and real-time security alerts to ensure that content remains protected, governed, and visible at all times. We will continue to deliver industry-leading data protection and governance capabilities to ensure the security of unstructured data in an enterprise. Now, we will be sharing much more about our product roadmap at this year's BoxWorks in San Francisco in early November, where we will be making major product announcements, we will hear directly from customers that are taking advantage of the Box platform and hear directly from our partners, including the CEO of NVIDIA, Jensen Huang, Lip-Bu Tan, the CEO of Intel, and Michael Truell, the CEO and founder of Cursor. Next, for our go-to-market strategy, we remain focused on accelerating the adoption of Enterprise Advanced, enabling customers to power their intelligent workflows with content while driving the growth of platform revenue. To win in key industries such as financial services, life sciences, government, education, media and entertainment, legal, and other key verticals, we will continue to deepen our vertical specific marketing, sales motions, collateral solutions, and ecosystem partnerships. We are also expanding our FDE or Forward Deployed Engineering efforts to ensure that customers can successfully implement and tune AI agents on their enterprise content for everything from document processing to agentic content workflows. Additionally, we are expanding our system integrator ecosystem, collaborating with vertical, regional, and global system integrators to embed our platform deeper into enterprises' critical content workflows. Finally, our partnerships with major hyperscalers like Amazon and Google will be central to expanding our enterprise distribution and enablement. In the second quarter, we continued to see strong momentum in customer wins enabled by these partners, a critical part of our go-to-market strategy. For instance, in partnership with DataBank, a leading insurance provider has adopted Box Enterprise Advanced with Box Shield Pro and purchased additional AI units to drive a comprehensive platform modernization. These deployments leverage Box's platform APIs, the Box Sign APIs, and Box AI to connect Box directly into the firm's custom middleware for core systems, including Guidewire. This positions the insurance provider to modernize more than 100 TB of content, retire multiple legacy platforms, and integrate Box AI across high volume workflows like mailroom and policy processing. Working with Slalom, a large U.S. state DMV upgraded from Enterprise Plus to Enterprise Advanced and purchased additional AI Units as the foundation for a new intelligent document processing initiative. This agency is replacing a costly legacy document processing system with classification and metadata extraction powered by Box AI. The solution will extract key information from identity documents at scale and automatically populate Salesforce records associated with each driver profile, streamline licensing applications and renewals across the state. At Box, we have an extraordinary opportunity to serve as the defining platform for securing, managing, governing, and applying intelligence to unstructured enterprise data at scale. Nearly all mission-critical workflows, such as processing regulatory data, automating insurance claims with AI, reviewing legal contracts, managing aviation research, or facilitating collaboration in pharma, are all fundamentally powered by enterprise content. Our intelligent content management platform sits squarely at the center of these vital business processes. We're incredibly excited at Box about the market transformation happening right now due to AI, and we have the team, the technology, and the customer base to fundamentally take advantage of this massive opportunity. Now, let me turn the call over to Dylan. Thanks, Aaron, and good afternoon, everyone. We had another very strong quarter in Q2, driven by record Q2 bookings and increasing Box AI adoption. As a result, we exceeded guidance across all top and bottom line results, delivering our fifth consecutive quarter of accelerating revenue growth in constant currency. As Aaron discussed, we advanced our leading intelligent content management platform by deepening our AI and agentic capabilities while investing in key go-to-market initiatives to drive continued Enterprise Advanced momentum. Q2 revenue of $321 million was up 9% year-over-year and up 11% in constant currency, exceeding our guidance. Customers paying us at least $100,000 annually grew by 10% year-over-year. Suites customers now account for 69% of revenue, up from 63% a year ago. We ended Q2 with remaining performance obligations, or RPO, of $1.7 billion, a 15% year-over-year increase, or 17% in constant currency. Both short-term and long-term RPO accelerated sequentially with short-term RPO up 11% year-over-year and up 14% in constant currency. We expect to recognize roughly 55% of our RPO over the next 12 months. Q2 billings of $310 million were very strong, growing by 17% year-over-year or 16% in constant currency. This result exceeded our expectations for low double-digit growth, with the outperformance driven primarily by Q2 booking strength. In Q2, our net retention rate improved to 106% above our guidance of 105% and up from 103% in the year ago period. Our annualized full churn rate remained at 3%. This outperformance was driven by continued improvement in our seat expansion rate, as well as the impact of very strong net retention results within our Enterprise Advanced customer base, which exceeded our overall net retention rate. We now expect our net retention rate to be 106% exiting FY 2027. We delivered Q2 gross margin of 81.2%, in line with our expectations. Operating income of $95 million resulted in operating margin expansion of 90 basis points from the year ago period to 29.4%, which reflects a 100 basis point headwind from FX. This was above our guidance of 28.5%. In Q2, we delivered EPS of $0.40, which was above our guidance of $0.39. This includes an FX headwind of $0.04, one cent higher than our prior expectations. Turning to our cash flow and balance sheet. In Q2, we generated free cash flow of $60 million and cash flow from operations of $71 million, up 67% and 54% year-over-year respectively. These results were driven by strong linearity, allowing us to collect a healthy portion of our Q2 bookings within the quarter. We ended Q2 with $446 million in cash equivalents, restricted cash, and short-term investments. In Q2, we repurchased 2.6 million shares for approximately $66 million. As of July 31st, 2026, we had approximately $378 million of remaining buyback capacity under our current share repurchase plan. With that, let me now turn to our Q3 and updated FY 2027 guidance. Note that our second half expenses will be more weighted toward Q4 versus our typical seasonality due to the expected impacts from BoxWorks occurring in Q4 this year, as well as the recent extension of our Redwood City headquarters lease. For the third quarter of fiscal 2027, we expect Q3 revenue to be approximately $329 million, representing approximately 9% year-over-year growth or 11% in constant currency. We anticipate our Q3 billings growth rate to be roughly in line with revenue growth of 9%, which includes an expected tailwind from FX of approximately 70 basis points. We expect Q3 gross margin to be approximately 80.5%. We anticipate Q3 operating margin to be approximately 28%, which includes an expected headwind from FX of approximately 80 basis points. We expect Q3 EPS to be approximately $0.39, which includes an expected headwind from FX of approximately $0.02. Weighted average diluted shares are expected to be approximately 142 million. For the full fiscal year ending January 31st, 2027, we are raising our revenue expectations for the full year by $10 million to approximately $1.29 billion, representing 10% year-over-year growth, or 11% in constant currency. We expect our FY 2027 billings growth to be roughly in line with revenue growth. This includes an expected headwind of approximately 150 basis points from FX. We expect FY 2027 gross margin to be approximately 80.5%, with Q4 gross margin expected to be roughly 80%. This reflects the strong and growing adoption of Box's platform and Box AI, as well as the capacity dynamics of our public cloud providers. We continue to expect FY 2027 operating margin to be approximately 28%, which includes an expected headwind from FX of 80 basis points. This reflects our ongoing focus on delivering operational efficiencies even as we continue to invest in driving durable revenue growth. We now expect FY 2027 EPS of approximately $1.54, which includes an expected headwind from FX of approximately $0.09. Adjusting for the impact of the currency and share count movements versus our previous expectations, this represents an increase of one cent versus our prior guidance. Weighted average diluted shares are expected to be approximately 141 million. This represents a significant reduction from 149 million shares in the prior year as we continue to execute our disciplined capital allocation strategy. The $10 million raise to our revenue expectations this year reflects continued momentum across the business with demand for Box AI and the growing adoption of Enterprise Advanced driving continued acceleration in our revenue growth rate and continued improvements in our net retention rate. As Box's intelligent content management platform is increasingly becoming the foundation enterprises rely on to securely unlock AI's value across their content, Box is well-positioned to drive durable long-term growth. With that, Aaron and I will be happy to take your questions. Operator? We will now begin the question and answer session. If you would like to ask a question, please press star one to raise your hand. To withdraw your question, press star one again. We ask that you pick up your handset when asking a question to allow for optimum sound quality. If you are muted locally, please remember to unmute your device. Please stand by while we compile the Q&A roster. Your first question comes from the line of Lucky Schreiner with D.A. Davidson. Your line is open. Please go ahead. Great. Thanks for taking my question. Aaron, I thought it was really interesting to hear about your expectations for innovation in the back half of the year, and I wanted to follow up on that. The longer agentic workflows tend to be more token-intensive. Can you give us an update on how you view token cost evolving here and how open source model adoption factors into customers implementing some of those longer form workflows and maybe sneak in any difference in unit economics between a frontier model versus open source for you guys internally? Thanks. Yeah. So you're exactly right. The kind of momentum we're seeing generally correlates to, interestingly, two dimensions, either one, longer running agents that do more processing work in a single session, or the ability to run agents off of large amounts of data, which can be broken up discreetly on a per item or per document basis. Both of which have the exact same type of tendency to be very token intensive, very consumption heavy, which is both great for us, basically on every dimension, because it means that customers will increasingly want those types of workflows to happen inside of platforms that are model neutral, because the more tokens your use case requires, the more obviously over time you're going to be price sensitive because you want to make sure that you're optimizing that cost structure for the use case. By having a model neutral layer, which is our agentic harness, we can then make sure that we are directing the workload to whatever is the effectively cheapest model at the accuracy level that the customer is looking for. In some cases, that can be an open weights model, and there are some models on the horizon that we are quite excited by that we will be opening up, we would assume in the second half based on some of the visibility we have from partners. In other cases, it can be just the sheer competition that is happening between the labs. You have seen things like OpenAI bringing down their prices or Gemini bringing down its prices. That actually also flows into our product as more either margin or relief or more consumption from customers. But in general, again, the trend that we are going to continue to see is customers are going to say, "I have millions, tens of millions, hundreds of millions of documents. I want to be able to run agentic workflows on these documents to automate processes or extract intelligence from data or be able to use all this information as critical knowledge for my organization." Our layer is really in the best position to both, again, deliver the highest level of accuracy at the cost profile that our customers are looking for. So you are going to see AI unit growth continue to go upward. You are going to see more upgrades into Enterprise Advanced, and even Box as a headless platform performs well in that environment as well. These are all great trends for us. Awesome. Last one from me. It was interesting to hear about the legacy migrations. Can you give us a sense, like those tend to be long and painful processes. Obviously, your partnerships improve that, but maybe how have you been able to speed up that process? I imagine Box Shuttle and AI capabilities in general help you out there, and what is the customer demand to go through that kind of painful modernization process today? Thanks. Yeah. It is interesting. We have obviously talked about this a bit over the past couple of years. I think it started out as something that we assumed and kind of could feel would happen, but it was still very early in the trajectory. Now we are actually seeing example after example on the rise, which is if you are an enterprise and you still have a large amount of your unstructured data in legacy systems or on-premises environments, your contracts, your research files, your insurance claim data, your loan processing documents, your KYC documents, your agency documents in a government agency, all of that data is often sort of effectively trapped and closed off from AI agents. As you have an AI strategy that assumes that an agent is going to read a document or process a claim or look through a contract or be able to process an image for brand guideline failures, all of that data needs to be available and accessible to AI agents in secure ways in these workflows. Many of the legacy approaches to doing document management or enterprise content management just simply do not work as companies are modernizing how they are going to work with and automate their enterprise content workflows. That is leading to this catalyst of more and more customers reaching out to us, calling us, and then obviously us going out into their environments for either large scale migrations or really kind of re-platforming their next generation of these workflows. We had a number of deals in Q2 that kind of represent this combination of very much agentic, workflow-driven use cases that have a data migration or a legacy ECM system migration as a part of where the dollars are going to come from. On the horizon, we actually see quite a bit of this opportunity continuing to grow. In the second half, and certainly as we go into next year, you will see a continued amount of product launches and updates that help facilitate and accelerate that migration. All of which are these next-generation features that help customers manage their content in the cloud at scale in these business processes, and be able to bring AI agents to that content very seamlessly and securely. Fantastic. Thanks for taking my questions, and congrats on a phenomenal quarter. Thank you. Your next question comes from the line of George Kurosawa with Citi. Your line is open. Please go ahead. Okay, great. Thanks for taking the questions. I am on for Steve Enders. Maybe just a question on the security role that Box plays within these agentic workflows. If you could talk about the governance and security side, what is maybe new or different in terms of enterprises' needs in an agentic world versus dealing with human users? How has Box's role there evolved? Yeah. This is an area of an incredible amount of surface area, which is very exciting for us. Obviously, it is an incredibly dynamic and sometimes kind of stressful space for the customer ecosystem, but there is a lot of innovation really available here. If you think about what agents tend to need, not only do they need basically all of the same level of security and controls that humans need. So they need access levels. They need to be only able to view or edit the documents that you want them to. You need to be able to obviously be alerted if they are either going rogue or you are seeing kind of too much usage happen. That is kind of the basic foundation that we are drafting off of in the core of Box's security and access control capabilities, which is why we are in a very strong position to take a lot of these workloads. But what is interesting is over time, they are actually going to need additional protections that we did not commonly think that end users needed. Things like a human user could really only process a certain amount of data at scale. So you could quickly kind of detect if maybe a user was doing something that they shouldn't be, or there would be very limited kind of blast radius or damage that they could do, if they kind of went rogue or if there was some malicious actor in the system. Whereas AI agents have the ability effectively with just, it's kind of just correlated to their compute level, to be able to either work with large amounts of data, access the wrong information, in the case of the Hugging Face or OpenAI, very much go out and execute on a goal to the kind of ultimate level that its compute is allowing. What that means is that enterprises are going to need an all-new set of guardrails, alerting mechanisms, anomaly detection capabilities, ways of really having a better set of controls on what agents can do with their data. That could be things like, agents inside of these folders or at these usage levels shouldn't be able to write data or shouldn't be able to download data. They can only kind of view it, or we need to be able to monitor their activity, or we need to be alerted after a certain threshold of activity happens under certain kinds of agents, and maybe we don't want to let these kinds of agents into our systems, but we're fine to let those other agents in the system. All of that functionality is effectively kind of net new for the agentic era. But what it's all built on is the same foundation and capabilities that we've been working on for a number of years. Shield, for instance, which is our advanced threat detection and security product from Box, we're going to continue to build out more and more features that help our customers protect this data, that both again let them protect human users and agentic users in the system. There's new forms of identity controls that we need to be building out that we're excited to share more about over the coming quarters. We obviously are going to continue to integrate with the broader security ecosystem to help protect enterprise data. But these are all things that continue to reinforce our value proposition and reinforce the need for secure, very robust systems of record on data. This is kind of why in maybe the first part of this year when there was a lot of commentary on maybe people will just vibe code different kinds of applications, to us, it was a little bit kind of humorous just because we know the level of security and protection that is necessary for these enterprise systems. I think as people saw the OpenAI Hugging Face incident, it kind of made it more resonate what's really possible out there from a security risk standpoint. So we take our position very seriously. We're going to be doubling down in all of our security investments and make sure that we are the best platform for helping customers protect all of this unstructured data. Okay, that's great color. Then maybe one for Dylan. You referenced a couple of different dynamics on the gross margin side, AI usage, and then capacity on the public cloud providers. If you could just double-click, what are some of the moving pieces there? How should we think about that going forward? Yeah. So as noted, really the two biggest drivers are the strong and growing adoption of Box's platform, Box AI specifically, as well as the capacity dynamics with our public cloud providers. To double-click a bit, really number one, we're really pleased with especially the new features, a lot of the heavier workloads and agentic processes that Aaron mentioned are certainly quickly growing and that new type of use case that customers use the platform for. Then on the capacity dynamics, that's really related to limited access to certain components of the infrastructure, just given what's going on in the environment. That really has an impact on the level and impact of the infrastructure efficiencies that we expect to deliver this year. So, certainly as those constraints ease, we'll continue to unlock those efficiency projects. But those are some of the things that we're seeing that are impacting gross margin versus our initial expectations entering the year. Your next question comes from the line of Matt Bullock with Bank of America. Your line is open. Please go ahead. Hi, yeah, this is Drake McOlash on for Matt Bullock. Thanks for taking the question. I think we are hearing more and more about enterprises going wall-to-wall. You mentioned a few on the call today. Could you talk specifically about how the AI governance or AI agent governance opportunity is driving this? Why is there a benefit to going wall-to-wall rather than just having Box maybe within a certain department when it comes to embedding AI workflows? Thanks. Yeah. Definitely, we have had some great wall-to-wall wins. I would say we are in a position where we can both capture the individual line of business use case that a customer is trying to automate or be able to bring AI to, as well as the wall-to-wall motion. I think, actually, both motions are humming at the moment. But to your point, the wall-to-wall benefits for a customer are really, imagine a scenario where you have rolled out a variety of AI agents to your enterprise. Maybe some people are using ChatGPT, others are using Claude Cowork. Maybe your developers are using Cursor, your Salesforce has Salesforce Agentforce. Now all of those agents are running around. They need access to corporate information to be able to make decisions or be able to work with your information. So that could be your research materials, your marketing assets, your HR documents, your contracts. The challenge is if you have three or five or 10 different systems where all of those agents all have to be able to work with and equally use successfully, and to be able to secure the access controls to all that data, it is just a very difficult problem. It is a many-to-many relationship problem, and that is generally not a clean architecture for most enterprises. So what you are going to see is a consistent pattern across various data planes where, how do we move our canonical data in different topics into sources of truth? So you have seen that in the structured data world with things like Databricks or Snowflake. We are seeing these kind of mass migration projects. You have obviously over the years seen that in ERP systems or CRM systems. But we also think there is quite a bit of momentum on the same for unstructured data. So you will go to an enterprise, and they will say, "Hey, I want to be able to have all these agents have access to corporate knowledge." That again is we need to make sure that those agents are working off of the real source of truth, the authoritative copy of data, which means that you cannot have a fragmented landscape that everybody is working from. So that is one of the core wall-to-wall benefits. That is the kind of knowledge worker, end user productivity benefit. There are also security benefits. There are governance benefits. We have been building a lot of features in Enterprise Advanced that help you with things like data retention, data archival. We have new capabilities around records management that we are excited to share in the next couple of quarters. But all of that is really around the need for robust infrastructure that helps you manage all that enterprise content, which is really the context for those agents. Yeah, and the only thing I'd add, this is Dylan, is as we talked about our confidence as we launched it in Enterprise Advanced, also being a potential catalyst for seat expansion because of the capabilities that it enables, and the types of workflows that customers can now do using those capabilities. A lot of those are really cross-departmental, right? So if you think about whether it's a sales enablement workflow or a contract life cycle management workflow or something like that, yeah, there might be a primary business unit who's really driving it, championing it, but that might go across four, five, six different departments. If they don't have access to Box, they're going to run into all the challenges that Aaron was mentioning. And that's why we've seen, you look at the increase in our net retention rate and those trends, the biggest driver of that has been higher seat expansion for exactly that reason. That's great. Thanks for taking the question, guys. Appreciate it. Your next question comes from the line of Chris Quintero with Morgan Stanley. Your line is open. Please go ahead. Hey, guys. Congrats on the nice acceleration here across the board. Aaron, maybe for you, high level, we've been hearing a lot about zero data retention as a key enabler for AI growth and compliance, and we've seen how some of the recent models have implemented some potential changes around that. So curious how you're thinking about that from the Box perspective and what's the opportunity there for you all? Yeah. This is obviously a very hot topic at the moment, mostly driven by the release of Fable. In general, maybe one of the underappreciated reasons for the rapid rise of AI growth is the fact that very quickly, most of the leading labs coalesced around the idea of zero data retention, which basically, in simplest terms, means that when I am interacting with an AI model and I have information in the context window, that data doesn't get stored and sit around for a week or 30 days in the servers of those AI labs. It's just an ephemeral pass-through. And that's what led to enterprises being very comfortable with the adoption of AI in their organizations, whether that was direct adoption or through more of these applied AI layers like a Box or a Harvey or Sierra, Decagon, et cetera. The challenge obviously with Sunlight Fable was they launched without zero data retention, which obviously means that then there can be less adoption. You have to have separate exception handling that customers have to go through. And we've made it very clear in our platform that the in-production GA, generally available models, will have a set of criteria that are met around zero data retention, certain compliance requirements, ways that the infrastructure is hosted, being able to have certain regions that it all operates in, and that's the enterprise trust that we've been able to establish with organizations as we deliver AI to them. That's again where I think you're going to see a huge benefit to this applied AI layer is being able to really bridge the breakthroughs of AI models with the actual real enterprise workflows that need compliance, they need guardrails, they need security, they need governance, they need these kind of regulatory controls. That's the state of the industry. My guess realistically is actually just Anthropic will evolve their stance on this because they'll see it in the revenue, and they'll have to change course, and I think we'll just see more and more modern approaches to how these companies will both meet their internal AI safety requirements with offering things that technically resemble ZDR for their users. Got it. Very helpful. Then great to hear about the FDE motion, the expansion you're putting through there. Curious, what are some of the key learnings you've had on that whole motion as you've now ramped it up and grown it over the past few months? Yeah. This is a pretty exciting area because if you think about the types of challenges, workflows, goals that customers have when working with enterprise content and unstructured data and agents. Let's say you're a bank, let's say you're a pharma company, let's say you're an insurance provider, you're seeing all of this potential with AI, and certainly in your internal use cases, things like coding agents, et cetera, are showing incredible breakthroughs and productivity gains. But the rest of your organization, the back office processes, the customer-facing workflows, you're trying to figure out, how do I actually get these same AI gains in those parts of the business? Some of that's a data challenge, some of that's an architecture challenge, some of that's an information governance challenge. All of which, obviously, Box is very, very expert in. But a lot of times it's also, how do I tune these agents? How do I pick the right model? How do I run evals against my data and compare what model is the best to use for certain types of workflows or certain kinds of document processing tasks? Box, from just a talent standpoint, a brand standpoint, and a technology standpoint, I think best represents being able to help customers go through that journey. The FDE motion really originated by us effectively doing that with a number of our individuals across our consulting team and our solutions engineering team. We've been increasingly more programmatizing that and scaling that across customers. I think this is going to continue to be an investment area for us, both for the rest of this year and certainly next year. And again, in essence, it is really about helping our customers transform with AI, making sure that they can actually get the data environment set up in the way that they need, making sure that they understand how to do the right types of evaluations of AI models and agents on their documents, continue to tune these workflows. There has been some engagements we have worked on with customers where we have gone back maybe nearly a half a dozen times because there has been a new model breakthrough, and we want to introduce that into the customer's environment and make sure that they can get those either productivity gains or cost savings as a result of that. And that really, again, requires a high degree of technical expertise on the side of both the vendor and the customer, which is why you are seeing this big push on FDEs right now. Excellent. Thank you so much. Your next question comes from the line of Brian Peterson with Raymond James Company. Your line is open. Please go ahead. Hi, guys. Thanks for taking the question. This is Johnathan McCary on for Brian. I will ask one just multi-parter. So, Aaron, I realize it is early days, but I wanted to ask on the consumption trends, it sounds like that is ramping nicely. How much of the traction there is customers that are seeing enough success to actually come back and refill the tank, so to speak, on credits prior to renewal? And then relatedly, is the ramping feature adoption there on the AI platform predominantly from an existing customers? Or are we at a stage where that is actually driving material competitive wins on the net new side as well? Thanks, guys. Yeah. Great question. Consumption is obviously off of a lower base than many other parts of the business, but has been growing, I think, quite rapidly, especially on a year-over-year standpoint. In Q2, we saw really nice wins on the growth side. A lot of it is a customer still doing their initial big growth workloads just because we're still relatively early in the AI Unit monetization. Then by virtue of just the scale of our existing customer base, a lot of it is from existing customers. What's interesting is that is often leading to materially bigger upsells on those existing customers. It's a little bit, might be even incidental in some cases that it's an existing account because the new transaction is so different or bigger because of the new use case that they have with us. We're seeing really great opportunities across the board, where customers are saying, "Hey, I thought of you as a place where I put my documents or I put my content. Now it's a place where actually I can run the workflow and intelligent workflow on you," which is really changing the calculus of the conversation. I was in a conversation just literally 3 days ago with a customer that has been at Box probably nearly a decade, and the moment we showed them our agentic workflow automation tool, it completely changed the type of conversation we were having. This is a customer that theoretically could have already known about all of those use cases and capabilities, but we caught them in it when they were at a juncture of a new set of use cases they need around their documents and document processing. That will absolutely turn into now another sales motion where we go and work with that customer to expand what they're doing with Box. So that's how it's working every single day right now across our customer base. What that's leading to is, almost back to the last question, more on the FDE front, more on the verticalization piece, more with system integrators, all of which we're seeing great success in from a go-to-market investment standpoint. Thanks, Aaron. Appreciate it. Our next question comes from Joshua Trautman with RBC Capital Markets. Your line is open. Please go ahead. Hi, team. This is Joshua Trautman on for Rishi Jaluria. Congrats on the quarter. You guys have mentioned strong product adoption, and some different tailwinds to retention. I was just curious around how international deals have helped participate in that, and how those conversations with those clients have been developing. Thank you. Yeah. Great. For us, it was a broad-based quarter. We're quite happy with the results. We had great wins in Japan, some of the leading enterprises out there that we're hopeful we'll be able to announce relatively soon some of those big wins. Some great wins in EMEA. We got great traction and participation from EMEA. Then obviously, U.S. business across everything from public sector, U.S. enterprise, commercial, great momentum, all of which very similar contours of conversation around how do you transform with AI agents. I was out in Japan in the beginning of June, and just overwhelming excitement around how do you bring more automation to your enterprise content with the power of AI agents. Again, those are very similar to the conversations that we're having here in the U.S. and those that we're having in Europe. Awesome. Thank you. Your next question comes from the line of Jason Ader with William Blair. Your line is open. Please go ahead. Yeah. Thank you. Good afternoon, guys. I wanted to ask about billings, very strong in Q2. Dylan, for Q3, I think you got it to 8%. Can you help us understand if there's some seasonality there? Why the step down from Q2 to Q3? Sure. A lot of moving pieces in there, and billings is inherently lumpy. If you look back to even entering the year, that's kind of the dynamic that we had expected, which is a combination of everything from very different FX impacts quarter to quarter to ease or difficulty of some of the comps. I would say, if you think about the full year billings outcome, even since our initial guidance 6 months ago, we've raised those expectations pretty significantly, more than $20 million, and showing a strong acceleration from where we were last year. I would really think about it as that dynamic as not at all indicative of the change that we're seeing in the demand and momentum in the business as the underlying leading indicators of that growth and bookings remain very healthy from pipeline to Enterprise Advanced adoption, to RPO and NRR, our net retention rate, both moving in the right direction. So really a function of I'd say just some of the variability quarter to quarter, as well as the fact that as always, we want to be thoughtful and prudent about how we guide. Even looking back to this year so far, we've had pretty significant outperformance against the guidance that we set up, and hopefully that trend continues. But just really wanted to be thoughtful about that. Okay. For the year, I think you said billings growth and revenue growth about the same. It would seem to me, given the momentum you have, that billings growth would be ahead of revenue growth just because it is more of a leading indicator. Can you just help reconcile that? Yeah. I would say, do expect it to be ahead on a constant currency basis as there is more of an FX impact to billings than revenue. When we did give the revenue, as we are talking about growing at roughly the same rate, that is on an as reported basis. But again, some of it is just the way that we think about setting expectations, and there is, as you would imagine, more variability in billings, plus just some of the dynamics around compares that did not show up in the same way as revenue. So I would say other factors outside of the underlying momentum are impacting each of those metrics a little bit differently. But to your point, we do expect billings growth to be a leading indicator of revenue, to be ahead of it if we keep up the momentum that we have been on. I think you see that type of dynamic, for example, in our short-term RPO growth, which is both a few points ahead of either of those metrics, and you are moving in the right direction and accelerating. Okay. Thank you. Good luck. Thanks. We've reached the end of the Q&A. I will now pass the call off to Cynthia Hiponia for closing remarks. Great. Thank you everyone for joining us again this afternoon. As Aaron mentioned, we're hosting BoxWorks in San Francisco on November 5th, and we'll be once again doing another investor relations product briefing at the event. Look forward to giving you more details, and we'll talk to you on our next earnings call. Thank you.