原始来源

Our Investment in Ando: Modern Messaging for Teams and Agents

Accel · Noteworthy ·

Ivan Zhou describes Ando's messaging platform for people and AI agents. He explains why coordinating agents across separate apps is difficult, and how shared conversations could help teams carry context across workflows.

一目了然

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shared context in AI-augmented collaboration

Shared context improves agent alignment over time

原始摘录

Every interaction helps the system better understand the team, making the agents in a workspace more aligned and more proactive.
上下文

Ando is building a new messaging product for that world. Humans and agents work side by side, with agents participating in conversations and taking action while respecting the same boundaries you would expect from human teammates. Instead of working with agents across a growing collection of isolated apps, teams can collaborate with them in one shared environment where context carries across conversations. That shared context becomes more valuable over time. And because Ando is designed to work across different agents and functions, its value grows as teams bring more AI into their workflows. A more complex agent stack only makes this common collaboration layer more important.

network effects of AI collaboration platforms

Platform value increases with agent stack complexity

原始摘录

And because Ando is designed to work across different agents and functions, its value grows as teams bring more AI into their workflows. A more complex agent stack only makes this common collaboration layer more important.
上下文

Ando is building a new messaging product for that world. Humans and agents work side by side, with agents participating in conversations and taking action while respecting the same boundaries you would expect from human teammates. Instead of working with agents across a growing collection of isolated apps, teams can collaborate with them in one shared environment where context carries across conversations. That shared context becomes more valuable over time. Every interaction helps the system better understand the team, making the agents in a workspace more aligned and more proactive.

AI agent collaboration challenges

Coordination complexity grows with AI agents

原始摘录

as AI-native work becomes more multiplayer, coordinating context, memory, and activity across a growing number of agents becomes exponentially harder.
上下文

AI agents are becoming increasingly capable partners across software development, customer support, legal, finance, and many other functions. Unlike humans, agents can process large amounts of context, retrieve information on demand, operate independently 24/7, and work in parallel at scale. Today, most of our interactions with agents remain limited, happening inside individual apps or one-to-one conversations. However,

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