Sana Agents - Enterprise AI Workflow Automation Platform

Title

Sana Agents - Enterprise AI Workflow Automation Platform

Content

In 1970, AI and robotics pioneer Hans Moravec observed that tasks that are difficult for humans can be easy for AI, and vice versa. Fifty years later, as we build Sana AI, we are rediscovering this paradox. Our goal is to create an AI agent for work with infinite capabilities—one that can process any amount of information, answer any question, assist with all knowledge work, and ultimately solve your most difficult problems autonomously. Key lessons learned building enterprise AI agents: Multi-step planning and reasoning are essential: Techniques like chain-of-thought, tree-of-thought, and reflection significantly improve query response accuracy. We've trained our custom agent solution R-4 to lay out a step-by-step plan to solve the query, execute the plan, and self-reflect on its output. Meetings are a vital source of truth: So much invaluable company knowledge gets exchanged verbally in meetings vs. written documentation. It's an untapped source of net new company data. With R-4, Sana AI can transcribe, summarize, index, retrieve, and analyze meetings via seamless integrations with Google Meets, Teams, and Zoom. Verified data is critical: Company knowledge goes stale fast. An AI agent is uniquely positioned to succeed where traditional knowledge management has failed, but only if it knows what knowledge is most up-to-date. We've designed verification, deprecation, and Q&A workflows to ensure the assistant always has access to the latest information. Vector search alone is insufficient: We need a rich knowledge graph to handle queries based on multiple data variables. Vector search cannot predictably solve requests like "List all companies with more than $5M in ARR we've met in Europe over the last 12 months." Models need a unified interface: The AI ecosystem moves so fast that committing to a single provider feels risky. We've built an underlying architecture enabling Enterprise customers to choose and switch between a range of state-of-the-art models. Integrations, permissions, and deployments are a maze: Most company data lives across 100+ enterprise tools. We've built a system that automatically handles granular access controls and permissions through out-of-the-box integrations, supporting deployment in private clouds. Customization is expected but cumbersome: Every team wants its own unique AI agent, but fine-tuning for each team's needs is difficult without an internal AI team. Our no-code UI setup lets users build custom assistants tailored to each use case in minutes. Assistants should be proactive: Sana AI needs to anticipate needs across entire workflows—for sales, this could mean automatically taking notes according to the company's sales methodology, drafting follow-up emails, and updating Salesforce. Humans are the benchmark: Users expect the assistant to match what a human expert could do at every turn. Giving users a step-by-step view, citing sources, and allowing corrections has helped us build trust.