Thank you for joining us today. This was a solid quarter. We believe the future of work is one where humans and AI collaborate with the right context, controls, and checkpoints. That's the foundation of AI Studio and our newly announced AI teammates. And customers use these capabilities in production, and are already delivering real productivity gains. I'm excited to share more about our AI platform in a moment. But first, let's turn to the financial highlights from the quarter. Q3 revenues were $201 million, growing 9% year over year, exceeding the high end of our guidance. We generated non-GAAP operating income of $16.3 million or an 8% operating margin, also exceeding the high end of our guidance. Our margin improvement reflects disciplined cost management and a thoughtful reallocation of spending towards high leverage areas while still preserving capacity to invest in our AI platform. Free cash flow was also strong at $13.4 million in the quarter, or 7% on a margin basis. Overall, NRR was 96%, a slight improvement across all cohorts from last quarter, even with a heavier volume of large, predominantly tech renewals this quarter. Retention within our monthly customer base is at a twelve-month high, reflecting the work we've done to strengthen customer satisfaction and in-product experience which Anne will share more about in a moment. We expanded with some key customers this quarter, including one of the largest multinational entertainment companies in the world, two of the largest Fortune 100 healthcare service providers, and several large tech sector customers, including a Fortune 500 People Cloud platform as well as a leading AI data platform. AI Studio delivered another good quarter with solid growth in sequential bookings, including early traction with self-serve users. I want to highlight two AI Studio wins that demonstrate how customers across industries are already using our platform to modernize mission-critical workflows. A global premium glassware manufacturer serving hospitality and luxury brands worldwide is using AI Studio to modernize core marketing workflows, from campaign management to rapid content generation. One of Europe's largest and most influential trade show operators is now leveraging AI Studio to digitize their planning processes, streamline cross-department collaboration, and accelerate decision-making on major strategic programs, including the OKR rollout. Asana, Inc. has pioneered three waves of work transformation: collaborative work management, workflow automation, and now AI transformation. These form the basis of our strategy to help companies on their journey to the agentic enterprise, unlocking productivity for teams as they deliver against their goals smarter and faster. Let's take a closer look at each of these waves. The first wave was all about solving the most fundamental challenge in modern work: creating clarity and accountability for a company's work. Many of our customers' work was once scattered across emails, spreadsheets, and disconnected tools. Asana, Inc. created the structure and visibility teams need to stay aligned and accountable with our collaborative work management platform. Powered by the WorkGraph, teams now have a shared understanding of who is doing what, by when, how, and why. The second wave centered on workflow automation. Once teams had this clarity, they also wanted to increase their velocity. Asana, Inc.'s no-code workflow builder makes it simple to standardize and automate repeatable processes, from marketing intake to engineering sprint planning. To make it easy, we created a workflow gallery to let you launch any workflow instantly, so teams get to value in a secure, fast, context-aware way. Now we're entering a new wave with AI transformation. Every customer I speak with sees the immense potential, but the reality is many AI projects today are just not delivering. In fact, a recent MIT report said as many as 95% of AI pilots are failing to deliver on their productivity promise. Why is that? Our point of view is that a lot of AI solutions and applications today lack three fundamental things. Number one, they lack context. If you think about the foundational models, they understand very little of the work that's actually getting done within your organization. They don't have the memory of how things have been solved in the past. They aren't properly informed, so they can't be accurate or effective. They also lack checkpoints. AI can't be trusted to just run an end-to-end process without having humans in the loop. But these checkpoints today are nonexistent in many of the tools. And finally, they lack controls. What we see and what I hear from executives is that AI agents often have more access to data than the employees themselves. Employees are carefully given things like role-based access control, but this somehow doesn't translate to the agents that they have in their organization, which, of course, is a risk. This is exactly what Asana, Inc.'s AI platform was built to solve. As a platform for human-AI collaboration, Asana, Inc. delivers the context, checkpoints, and controls that enterprises require. The context is really the who, the what, the when, the why, and the how. Context is king. If you want AI to be useful, it needs to understand how this was solved in the past, who was involved, who needs to be involved next, and what approval process we need to have. Thanks to the WorkGraph data model, Asana, Inc. can give AI this rich and relevant context. And you also need these checkpoints. It's our strong point of view that humans need to be in the loop on most of these AI processes to approve things and course correct if needed. In Asana, Inc., you can check the quality of AI's work and iterate with it so that it will continue to improve. And finally, unique controls. We have a strong belief that AI agents should not have more access to data than your regular teammates. They should follow the same governance process, the security controls, the access to proprietary customer data that you've already established. These are the three C's as we call them: context, checkpoints, and controls. Another foundation of our AI platform. Built on that foundation of context, checkpoints, and controls, the first AI add-on product we introduced was AI Studio. Think of AI Studio as supercharging your workflows. I like to think of it as inserting nodes in a given workflow to consult those foundational models, but doing so in the context of that workflow and the context of that work. It allows you to insert AI nodes directly into the process to handle specific tasks. For example, an intake node can check an incoming request, see if it's missing a due date, and assign it back to the requester automatically. A triage node can analyze that request against your company's goals and flag it as high priority. A quality node can check a deliverable against the predefined knowledge base to ensure it meets specifications. A risk monitoring node can proactively flag when tasks are falling behind schedule well before they cascade, and a translation node can automatically convert content for global markets and route it to the right local teams. This is our first step towards enabling the agentic enterprise, ensuring smarter, faster delivery of your goals. Which brings us to the next strategic step in building an agentic enterprise: enabling AI not just to coordinate work, but to do the work alongside your teams as a true collaborative teammate. At our Work Innovation Summit events in London and New York, we announced Asana, Inc. AI teammates, and we're already receiving strong positive feedback from our initial set of 30 beta customers. We expect AI teammates will be generally available early next year. Asana, Inc. AI teammates are collaborative agents to help you deliver real business outcomes. They remember, take action, and adapt with full context on projects, goals, and how your teams operate. They show their work and have built-in checkpoints you can course correct. And with our AI teammates, you are always in control. And we've already built out 12 out-of-the-box teammates across engineering, IT, marketing, operations, and PMO. These aren't prototypes or demos. They're prebuilt, tested, and ready to deploy. Each teammate operates with full context from the WorkGraph, clear checkpoints for human guidance, and enterprise-grade controls. Marketing teammates that manage campaigns and create content, PMO teammates that track projects and flag risks, IT teammates that triage tickets and optimize resources. Customers can also build their own AI teammates in minutes by defining the role, connecting it to the right data sources, and refining behavior through checkpoints. This makes agentic AI accessible to every team, not just technical teams. So what makes our agents different? Well, first, they have context powered by the Asana, Inc. WorkGraph. They understand from the moment they are assigned how your work gets done, how you solve problems before, and exactly who to involve. That context allows them to be truly collaborative. Because they understand the work, they can actively drive execution alongside people in the team. And, crucially, are multiplayer by design. Our teammates publish their plans openly. This creates a shared transparent starting point where everyone can provide feedback, ensuring the AI learns from your team's collective input. The early response from customers has been positive and reinforces the opportunity ahead. Morningstar, an early adopter of Asana, Inc. AI, is leveraging AI teammates to tackle complex strategic work that requires deep analysis and human-like reasoning. By creating a specialized AI teammate, the product management team was able to complete tasks that normally take up to two weeks in just ten to twelve hours. Level Agency has built AI teammates for marketing to act as a workflow accelerator, saving three to five hours per content project, as well as for IT to help triage support tickets and company-wide to intelligently review and operationalize new process ideas. Also using AI teammates internally at Asana, Inc., our product, design, and engineering teams use a Figma and CursorTeammate to convert prototypes into production-ready UI code in roughly fifteen minutes with more than 90% accuracy. Our brief buddy teammate supports every marketing kickoff and removes about an hour from the start of a project. And our marketing team's localization teammates achieved roughly 90% language parity at about half the cost, allowing us to reinvest savings into SEO and global growth. These examples reinforce that agentic AI is not theoretical; it's already improving how we run our own business. Across all these deployments of our AI products, trust is central. Teammates only see the data they're explicitly granted access to. Admins control who can create or modify them. And customers have full visibility into the usage and cost with the ability to set limits to ensure strong ROI. We built these capabilities into AI Studio and teammates from day one because enterprise adoption depends on governance as much as capability. This focus on enterprise-grade AI is the foundation of our strategy. But in addition, operational priorities I led up last quarter remain consistent. And our Q3 results show clear measurable progress against them. First, as I shared last quarter, when we go deep in this vertical, speak the language of that persona, and align to the core workflows of that industry, we win. I want to highlight this playing out in a meaningful way. For example, in the healthcare vertical, where we closed several marquee expansions this quarter. One of the largest health insurance organizations in the US expanded to more than 3,000 seats and crossed the $1 million ARR threshold. It standardized their clinical service intake across a 20,000-person business unit and is expanding into adjacent teams like analytics, pharmacy, and student health, all running mission-critical workflows in Asana, Inc. Another major diversified healthcare company, now well above $1 million ARR with us, embedded Asana, Inc. deeply in their Medicaid organization to support the creation and expansion of Medicaid managed care plans across local markets. Asana, Inc. is also being adopted across many new teams. And a global pharmaceutical leader selected Asana, Inc. as a preferred PMO solution, including for mobile clinical trial workflows, an innovation model designed to expand trial access to patients far beyond traditional sites. These telco wins are a testament to Asana, Inc.'s ability to adapt to industry-specific solutions with ease. Second, we're improving go-to-market execution and value realization across our sales and self-service motions. We believe our focus on customer health improvement initiatives is contributing directly to the continued improvement of NRR. We delivered our strongest retention in more than a year amongst monthly self-service customers, an encouraging signal that our self-service engine improvements across product experience and support are taking hold. Third, we're maintaining our focus on disciplined, profitable growth. Our significant improvement in non-GAAP operating margin and strong adjusted free cash flow are direct results of this discipline. Our operating leverage and continued commitment to driving higher productivity from our cost base gives us the capacity to keep investing in high leverage areas, especially our AI platform, all while expanding our margins. Next quarter, I look forward to sharing additional proof points, customer stories, and outlining the FY '27 priorities that I believe will support long-term growth acceleration and continued margin expansion. Before I hand over to Anne, I also want to take a moment to share that Anne will be leaving Asana, Inc. after seven years. I'm so grateful for all that Anne has done. Anne has played a major role in our story, first as a highly engaged board member, and then as our COO and head of business. She was instrumental in building an enterprise go-to-market motion and serving as one of our most trusted customer voices. I personally want to thank Anne for her leadership, partnership, and the foundation she's helped shape across our product, customer, and field teams. As we look ahead, given our size, scale, and the need to drive tight alignment across product, product-led growth, sales-led growth, and marketing, our go-to-market leaders, including the CRO and CMO, will now report directly to me, and we'll not be backfilling the COO role. This structure strengthens our ability to move with speed and focus, and it best positions us to accelerate growth over the long term and capitalize on the large and growing opportunity we see in leading the market for human-AI collaboration. I'm grateful for everything Anne has done to help us get to this point, and wish her the very best. With that, I'll turn it over to Anne.