This is Brad DeLong's Grasping Reality—my attempt to make myself, and all of you out there in SubStackLand, smarter by writing where I have Value Above Replacement and shutting up where I do not… Bloody-Minded Software-Entity Bolshyism in the Agentosphere: Laugh of the Day<http://every.to> has been experimenting with team‑wide AI agents shows that without discipline, transparency, and audit trails. It has found that “helpful” LLM-based agentic ‘bots quickly become...
<http://every.to> has been experimenting with team‑wide AI agents shows that without discipline, transparency, and audit trails. It has found that “helpful” LLM-based agentic ‘bots quickly become surly time thieves. Unleashing opaque, non‑deterministic software entities into production workflows without the institutional scaffolding that makes their work legible and correctable has results—and I would say they turn out to be ones that should have been anticipated…Every <http://every.to> grapples with the robot uprising!
There were also other problems—not deeper problems than bloody-minded bolshy non-coöperativeness, but problems nonetheless. Problems connected with people’s desires to spend their time doing their actual jobs rather than, as amateurs, debug buggy non-deterministic software entities that nobody understands:
They are trying again:
Perhaps I can offer them a word of advice? This may well be the Excel vs. STATA wars back again: Serious work requires an audit trail, and tools that do not give you an easily accessible and comprehensible audit trail are tools that produce GIGO. But how to give these things an audit trail? My view:
“AI agents” promise to handle workflows across email, tickets, repos, and calendars, but in practice they fail noisily, opaquely, and in ways nobody quite understands. Treating them as magical coworkers is the mistake. Instead, we should treat them as non‑deterministic software entities embedded in a system that insists on traceability: one agent builds tools, others validate them, additional agents report precisely what the tools did, all writing into a single, version‑controlled workspace. Humans then inspect that ground truth before anything hits a calendar or a database. Research and summaries? Fine. Autonomous writes? Not yet. Basically: always be diving and looping from the LLM ‘bot interaction two layers abstraction layers further down to see what is actually going on. And that will usually be that it does not hit the bullseye when the task has one and only one unambiguously correct conclusion, unless the LLM ‘bot is tightly constrained to act on data and on the world through audited program-tools. If reading this gets you Value Above Replacement, then become a free subscriber to this newsletter. And forward it! And if your VAR from this newsletter is in the three digits or more each year, please become a paid subscriber! I am trying to make you readers—and myself—smarter. Please tell me if I succeed, or how I fail…##bloody-minded-software-entity-bolshyism-in-the-agentosphere-laugh-of-the-day
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