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View original post on X: Harrison ChaseX· 40/100AI score40/100

LangChain outlines managed deep agents with durable workflows, sandboxes, and observability

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Harrison Chase says his summary slide shows how future agents will look and that it aligns with LangChain's managed deep agents vision.

The slide lists durable workflows built on LangGraph and LangSmith Deployment, computer use through LangSmith Sandboxes, memory and skills through LangSmith Context Hub, schedules, connectors, text and voice channels, and evals.

He says payments and identity are the only missing piece.

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Harrison ChaseVerified on X
@hwchase17

this is the summary slide that describes how all agents in the future will look

very much aligns with our vision for managed deepagents https://docs.langchain.com/langsmith/python/managed-deep-agents-overview

durable workflows: builds on LangGraph/LSD https://docs.langchain.com/langsmith/deployment

can use a computer: uses Langsmith Sandboxes https://docs.langchain.com/langsmith/python/managed-deep-agents-sandboxes

memory and skills: langsmith context hub https://docs.langchain.com/langsmith/python/managed-deep-agents-skills

Triggers and scheduled jobs: schedules https://docs.langchain.com/langsmith/python/managed-deep-agents-schedules

connectors and auth https://docs.langchain.com/langsmith/python/managed-deep-agents-connections

text and voice channels https://docs.langchain.com/langsmith/python/managed-deep-agents-channels

payments and identity - only one missing!

evals https://docs.langchain.com/langsmith/python/managed-deep-agents-evals

observability: massively, through langsmith https://docs.langchain.com/langsmith/observability

Lee Robinson@leerob
How do always-on, proactive agents like @Bot work? Watch my lecture at Stanford CS146S: 2:35 How we got here 6:13 What changed in the models 10:06 Inside an always-on agent 18:07 The harness 27:26 Context engineering 34:26 Where this is going
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Source: Harrison Chase · x.comPublished · added here