AI & Agents, Agentic Loops, Quality Gates, and Content Pipelines
The complete cluster on agentic AI workflows, state-machine loops, and quality gates for AI content pipelines. Why prompts fail and loops survive.
AI & Agents
State-machine loops, validation gates, and the architecture behind AI systems that actually produce work, not just impressive demos.
Prompts are static documents. Models ignore them under context pressure. Loops are dynamic processes. They validate at every transition and recover from failure in seconds rather than hours. This cluster is the architecture behind Spiel OS and every serious AI content pipeline I have built.
If you have ever lost an afternoon to a model that quietly ignored half your rules, this cluster is for you.
Start here
The pillar post that anchors the cluster.
All posts in this cluster
From architecture to receipts.
From Declarative Rules To Agentic Loops
Why your LLM agents ignore half your rules. Learn the agentic AI workflow architecture that replaces fragile prompts with state-machine loops that actually work.
AI Content Pipelines Need Quality Gates, Not Just Better Models
Why AI content automation fails without quality gates, and the handoff pattern that keeps automated content pipelines producing work that sounds human.
How I Rebuilt My Blog in 4 Hours With a Jekyll Automation Pipeline
A Jekyll blog automation pipeline that turns vault drafts into live posts with one command. From empty demo to brand blog with 2 posts shipped the same night.
Is GLM-5.2 Cheaper Than the Frontier?
A working breakdown of LLM API pricing across Anthropic, OpenAI, Google, and Z.ai, at every reasoning effort. Where the popular claim breaks down, where it could technically be true, and what the data says about intelligence.
Get the tooling
Two loops. One architecture. Open source.
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