@Vtrivedy10: "Mental Model: An all-knowing AGI Agent is really a perfect, just-in-time workflow generator & executor." was messing a…

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Summary

A tweet discusses a mental model where an AGI agent functions as a just-in-time workflow generator and executor, noting that Anthropic's Dynamic Workflows are an early implementation of this concept.

"Mental Model: An all-knowing AGI Agent is really a perfect, just-in-time workflow generator & executor." was messing around with this idea late last year and Anthropic's Dynamic Workflows "feel" like the first implementation of the mental model where the models are intelligent enough to take advantage of this problem decomposition strategy (maybe possible since January) dynamic workflows - just-in-time decompose complex problems into workflow primitives via code gen - assign large amounts of compute to solve sub-problems - BUT adaptively alter the execution plan for the workflow based on learnings from sub-executions imo AGI is just doing this flow perfectly including any exploration and verification steps. Generating & execute the right workflow for any input task, across any time horizon design primitives like dynamic workflows & /goal feel like exciting sparks of the generalizable problem solving machine where the UX maps onto how humans want to interact with AI even if the exact implementation today may not be "the one" and may even often look like slop... the trajectory feels correct
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Cached at: 06/03/26, 07:54 PM

“Mental Model: An all-knowing AGI Agent is really a perfect, just-in-time workflow generator & executor.”

was messing around with this idea late last year and Anthropic’s Dynamic Workflows “feel” like the first implementation of the mental model where the models are intelligent enough to take advantage of this problem decomposition strategy (maybe possible since January)

dynamic workflows

  • just-in-time decompose complex problems into workflow primitives via code gen
  • assign large amounts of compute to solve sub-problems
  • BUT adaptively alter the execution plan for the workflow based on learnings from sub-executions

imo AGI is just doing this flow perfectly including any exploration and verification steps. Generating & execute the right workflow for any input task, across any time horizon

design primitives like dynamic workflows & /goal feel like exciting sparks of the generalizable problem solving machine where the UX maps onto how humans want to interact with AI

even if the exact implementation today may not be “the one” and may even often look like slop…

the trajectory feels correct

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