公开观点

Ivan Zhou

Source author · Accel

1 场访谈 · 3 条观点 · 3 个话题

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按访谈日期排序的归属观点。这是相关对话的快照,而非对某人信念的最终定论。

AI agent collaboration challenges

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Coordination complexity grows with AI agents

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

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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.
Our Investment in Ando: Modern Messaging for Teams and Agents
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,

位置来自原始文本;请结合原文语境核对措辞与归属。

shared context in AI-augmented collaboration

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Shared context improves agent alignment over time

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

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原始摘录

Every interaction helps the system better understand the team, making the agents in a workspace more aligned and more proactive.
Our Investment in Ando: Modern Messaging for Teams and Agents
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.

位置来自原始文本;请结合原文语境核对措辞与归属。

network effects of AI collaboration platforms

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Platform value increases with agent stack complexity

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.

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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.
Our Investment in Ando: Modern Messaging for Teams and Agents
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.

位置来自原始文本;请结合原文语境核对措辞与归属。

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按原始访谈发布日期排列;表述不同不代表立场发生变化。