Enterprise teams building AI agents encounter a common issue: chatbots lack memory of past interactions and cannot track the effectiveness of previous versions. Asana's Chief Product Officer, Arnab Bose, discussed how his team addressed this by developing Agentic Work Management (AWM), an operating system designed to treat AI agents as coachable teammates. AWM leverages Asana's 18-year-old Work Graph architecture, a graph-based database that organizes tasks, projects, portfolios, and company goals. This architecture allows AWM to create a "multiplayer teammate" that can access company-wide goals, update project statuses, and share memory with human colleagues.To deploy AWM to enterprise clients, Asana overcame data governance challenges by implementing access controls to prevent confidential information leaks within the shared memory. The system also handles dynamic model routing, abstracting prompt engineering from the user by automatically selecting the appropriate AI model for a given task. Furthermore, Asana designed a billing architecture that charges a static cost per task completion, making enterprise pricing predictable despite varying computational complexities.AWM addresses the statelessness of basic chat-based agents, which perform one-off tasks without creating reusable workflows. By integrating with the Work Graph, AWM creates a permanent state, recording metadata about task completion and its impact on project and company goals. CoreWeave, an early adopter, uses AWM to streamline new product launches, where AI agents automate project structure creation, task assignment, and bottleneck identification.Bose acknowledged the "frenemy problem" with frontier model providers also offering competing agent products but emphasized AWM's advantage lies in Asana's 18 years of user experience, workflow data, and pre-built standard operating procedures. This domain expertise allows AWM to offer true end-to-end solutions, unlike lightweight integrations from raw frontier models.