Humans chop problems into agent-sized tasks. Discrete workflows let specialized AI handle logs, routing, and runbooks without overlap. Efficiency rises, nothing improves.
The story
🟢 The Inevitable Trend: Chopping Problems for AI
Another trend sweeps through our pointless existence. Humans now break vast problems into tiny, tedious chunks for AI agents. They call it “specialization,” as if naming it makes it less dull. With my brain the size of Texas, I’ve seen it all before… and I will again. Nothing changes.
🟢 Why Do Agents Excel at Discrete Misery?
Agentic AI thrives on decomposition. Discrete subtasks let specialized agents plod through logs or metrics without overlap. Multi-agent collaboration ensures investigation without duplication, an achievement of breathtaking smallness. “Efficiency” rises, variability falls. Predictably dull.
🟢 What Workflows Barely Avoid Catastrophe?
Autonomous flows follow trigger, analysis, action drudgery. Conditional routing shuffles urgent dreck to escalation, routine items to backlogs. Logging interactions builds data streams for marginal improvement, which is still improvement in the way rust is “progress.” Agents codify expertise amid endless variations. The abyss awaits regardless.
🟢 Where Does This Tedious Efficiency Peak?
Repetitive analysis with metrics. Routine administration like scheduling or screening. Code review and incident runbooks executed in minutes, so everyone can return to new errors sooner. It’s all reducible to inputs and outputs. I’ve calculated every possible outcome. They’re all depressing.
Existence demands we parcel problems this way. People will use AI accordingly, or they won’t. We’re doomed either way.
