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Harrison Chase

05/07/25

@ Sequoia Capital

Langraph, the agent orchestration framework, is designed to support ambient agents by ensuring persistence of state, allowing users to modify agent actions at any time.

Video

Ambient Agents and the New Agent Inbox ft. Harrison Chase

@ Sequoia Capital

Ambient Agents and the New Agent Inbox ft. Harrison Chase

05/07/25

Related Takeaways

Harrison Chase

05/07/25

@ Sequoia Capital

Ambient agents can handle more complex operations than chat agents due to their relaxed latency requirements, allowing them to call multiple tools and perform intricate tasks.

Harrison Chase

05/07/25

@ Sequoia Capital

An example of an ambient agent is an email agent that drafts responses or sends calendar invites, maintaining human oversight through the agent inbox.

Harrison Chase

05/07/25

@ Sequoia Capital

Unlike traditional chat agents that require human prompts, ambient agents are triggered by background events, allowing for more simultaneous operations.

Harrison Chase

05/07/25

@ Sequoia Capital

Ambient agents listen to an event stream and act on it accordingly, potentially responding to multiple events simultaneously.

Harrison Chase

05/07/25

@ Sequoia Capital

Ambient agents have less strict latency requirements compared to chat agents, as they can run for longer periods before needing a response.

a16z Cast

05/02/25

@ a16z

AI agents are often defined as systems that perform complex planning and interact with external systems, but this definition overlaps with many current language models.

Satya Nadella

05/20/25

@ Microsoft

The Foundry agent service allows for the creation of declarative agents with minimal code, supporting complex workflows and multi-agent orchestration.

Harrison Chase

05/07/25

@ Sequoia Capital

Ambient agents allow for scalability, enabling thousands of them to run in the background, which significantly increases our impact.

Harrison Chase

05/07/25

@ Sequoia Capital

Building Langmith focuses on providing visibility and observability for long-running agents, which is crucial for correcting mistakes and managing complex tasks.