May 25, 2026
AI gospel or AI word salad?
Agentic Patterns
AI builders say everyone is using the same playbook — commenters say it’s just fancy slop
TLDR: The article says top AI companies are all ending up with the same design rules because the problems are the same everywhere. Commenters mostly turned it into a roast, arguing the essay sounded overblown, vague, and more useful for companies selling AI than for regular builders.
A big new essay called Agentic Patterns tried to make one very bold point: the biggest AI toolmakers all keep landing on the same basic setup. In plain English, the author says this isn’t copycat behavior — it’s because building useful AI assistants runs into the same hard limits every time, from memory issues to safety rules to rising costs. The piece lays out eight must-follow rules, from keeping a standing instruction file to setting spending limits and breaking big jobs into smaller ones.
But the real fireworks were in the comments, where readers were not exactly bowing down. The strongest opinion by far? A pile-on calling the whole thing “slop” — internet shorthand for polished-looking writing people think says very little. One reader mocked the essay’s dramatic line about “physics,” while another dismissed the whole package as “load-bearing slop,” which honestly sounds like a fake startup name and a devastating insult at the same time. The mood was less “wow, useful blueprint” and more “did ChatGPT write this in a blazer?”
Still, not everyone was rejecting the idea itself. One of the sharper critiques argued that these may be the best patterns for vendors selling AI products, but not necessarily the best ways for everyone else to build. That turned the thread into a classic tech food fight: universal truth or corporate self-justification? Add in jokes about the essay’s stiff writing style — especially the overdramatic “it is not X, it is Y” phrasing — and the comments became a roast session with a side of architecture debate.
Key Points
- •The article says major agentic AI systems from different companies are converging on similar architectures because of shared technical constraints.
- •It groups agentic systems into three types: domain context substrates, personal AI runtimes, and multi-agent shells.
- •The article presents eight postulates for production systems, covering instruction files, safety enforcement, context budgeting, tool protocols, shared state, task decomposition, cost tracking, and incremental complexity.
- •It recommends MCP as the default protocol for tool integration and A2A for communication across systems or organizations.
- •The guide is framed for multiple audiences, including agent developers, platform engineers, infrastructure teams, and engineering managers.