Sequoia Backs Agentic AI Governance

The Agentic Access Management (AAM) framework, developed by Oasis Security in collaboration with Sequoia Capital and leading CISOs, provides a seven-pillar model to govern the machine-to-machine access layer of enterprise AI. This initiative addresses the rapid adoption of AI agents in enterprises, which, according to Oasis Security's customer data, has grown 840-fold year-over-year. The framework is vendor-neutral and aims to provide actionable guidance for securing the increasing number of non-human identities (NHIs) within organizations. Enterprise AI procurement is a detailed process that begins with a needs assessment involving business leaders, IT, and end-users to define the problem AI will solve. This is followed by market research, vendor evaluation based on scalability and integration, and pilot testing to see the solution in a real-world environment. The process concludes with contract negotiations, focusing on pricing and service-level agreements. For a successful integration, it's crucial to start with a small pilot project, ensure high-quality data, and involve key stakeholders. When selling to enterprise sales leaders, it's crucial to demonstrate a clear return on investment and articulate how the AI tool solves specific business problems rather than just highlighting its technical features. Successful vendors often build trust by first identifying the limitations of their products. Sales leaders at F500 companies are increasingly looking for AI tools that can enhance customer experience through hyper-personalization and improve operational efficiency. With AI skill requirements for sales and marketing roles in Fortune 500 companies increasing by 24% year-over-year, there is a clear demand for tools that augment team capabilities. Agentic AI architectures often utilize multi-agent systems, where complex problems are broken down into smaller tasks handled by specialized agents. Common orchestration patterns that define how these agents interact include the supervisor pattern for centralized control and the adaptive agent network for decentralized collaboration. The choice of pattern impacts token consumption, latency, and scalability. Agentic patterns are foundational blueprints for designing and orchestrating goal-oriented AI agents. The San Francisco Bay Area continues to be a dominant hub for AI startup funding, attracting 73% of all AI-related venture funding in North America since early 2024. In 2023, Bay Area startups secured over $27 billion, more than half of the global total for AI startups. This trend continued into early 2026, with a significant influx of capital into AI infrastructure, hardware, and robotics. However, investors are becoming more selective, prioritizing startups with clear product-market fit and scalable technology. For founders navigating the demanding startup environment, personal productivity frameworks like time blocking, batch processing similar tasks, and setting clear boundaries are essential for maintaining focus and preventing burnout. Consistent routines for sleep, exercise, and nutrition are also vital for long-term cognitive performance and resilience. Utilizing AI-powered tools for task management, such as Motion or Sunsama, can help automate scheduling and create a more mindful workflow. Emerging trends at the intersection of AI and hardware include the growing demand for GPUs for training and running complex models, which is driving the development of decentralized computing networks. In the crypto space, AI is being integrated to enhance security, power algorithmic trading, and automate regulatory compliance. The combination of AI and decentralized physical infrastructure networks (DePIN) is poised to address the high costs and shortages in the GPU-as-a-Service market.