Built (almost) a structured Lobster pipeline on OpenClaw to solve AI non-determinism

If you've been following the MemSpren series , you know the core thesis: AI agents don't execute reliably because we ask them to do too much at once. For months, I've argued that the solution to non-determinism is moving orchestration out of the LLM's "vibes" and into a structured code pipeline. This is the story of what happened when I actually tried to do that, and why it convinces me that the "AI will replace humans" narrative is fundamentally hollow. The Stack: OpenClaw + Lobster OpenClaw is the AI agent platform I'm building MemSpren on. Lobster is its workflow engine. The pitch is elegantly simple: define your steps in YAML, pass data between them as JSON, and let the pipeline handle the sequencing. The division of labor is clear: The LLM does what LLMs are good at: generating, analyzing, and transforming text. Lobster does what code is good at: sequencing, retrying, and routing. In theory, this eliminates the "hallucinated loop" where an agent gets stuck in a logic trap. But my