"background job" is actually the more honest framing.

the interesting design question you're pointing at, what happens when it wants attention, is where the real complexity lives. in practice i've found three patterns: (1) fire-and-forget with a completion webhook (2) structured checkpointing where the agent emits intermediate state that a supervisor can inspect (3) interrupt-driven where the agent can escalate blockers to a human or another agent mid-execution.

most "async agent" products today only implement (1) and call it a day. But (2) and (3) are where the actual value is, being able to inspect a running agent's reasoning mid-task and course-correct before it burns 10 minutes going down the wrong path.

the supervision protocol is the product, not the async dispatch.

Marvin Minsky thought of it a long time before Gas Town, and yes, he was on to something.

https://en.wikipedia.org/wiki/Society_of_Mind

>The Society of Mind is both the title of a 1986 book and the name of a theory of natural intelligence as written and developed by Marvin Minsky.

>In his book of the same name, Minsky constructs a model of human intelligence step by step, built up from the interactions of simple parts called agents, which are themselves mindless. He describes the postulated interactions as constituting a "society of mind", hence the title. [...]

>The theory

>Minsky first started developing the theory with Seymour Papert in the early 1970s. Minsky said that the biggest source of ideas about the theory came from his work in trying to create a machine that uses a robotic arm, a video camera, and a computer to build with children's blocks.

>Nature of mind

>A core tenet of Minsky's philosophy is that "minds are what brains do". The society of mind theory views the human mind – and any other naturally evolved cognitive system – as a vast society of individually simple processes known as agents. These processes are the fundamental thinking entities from which minds are built, and together produce the many abilities we attribute to minds. The great power in viewing a mind as a society of agents, as opposed to the consequence of some basic principle or some simple formal system, is that different agents can be based on different types of processes with different purposes, ways of representing knowledge, and methods for producing results.

>This idea is perhaps best summarized by the following quote:

>What magical trick makes us intelligent? The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle. —Marvin Minsky, The Society of Mind, p. 308

That puts Minsky either neatly in the scruffy camp, or scruffily in the neat camp, depending on how you look at it.

https://en.wikipedia.org/wiki/Neats_and_scruffies

Neuro-symbolic AI is the modern name for combining both; the idea goes back to the neat/scruffy era, the term to the 2010s. In 1983 Nils Nilsson argued that "the field needed both".

https://en.wikipedia.org/wiki/Neuro-symbolic_AI

For example, combining Gary Drescher’s symbolic learning with LLMs grounds the symbols: the schema mechanism discovers causal structure, and the LLM supplies meanings, explanations, and generalization—we’re doing that in MOOLLM and spell it out here:

MOOLLM: A Microworld Operating System for LLM Orchestration

See: Schema Mechanism: Drescher's Causal Learning

https://github.com/SimHacker/moollm/blob/main/designs/LEELA-...

Also: LLM Superpowers for the Gambit Engine:

https://github.com/SimHacker/moollm/blob/main/designs/LEELA-...

Schema Mechanism Skill:

https://github.com/SimHacker/moollm/blob/main/skills/schema-...

Schema Factory Skill:

https://github.com/SimHacker/moollm/blob/main/skills/schema-...

Example Schemas:

https://github.com/SimHacker/moollm/tree/main/skills/schema-...