Grounding, harnesses and reliable execution
How context, workflow structure and deterministic control change what agents can do reliably.
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I Took the Order Away From the Agent. Then I Learned Order Wasn't Enough.
Anchor stopped my agent from skipping declared steps. Ratchet came from the next question: after an interrupted or overlapping run, could I prove what happened and restart without making it worse?
Building Ratchet Runtime: What a Safe Restart Has to Prove
A technical look at the small execution runtime I built for overlapping and interrupted agent workflows: ownership, effect recovery, verified completion and the limits of a local alpha.
My Agent Kept Skipping Steps. The Fix Was Taking the Order Away From It.
A repeatable agent workflow kept skipping different steps on different runs. The fix wasn't a stronger prompt; it was moving the plan into a DAG and letting deterministic code control sequence and verification.
Most Teams Reach for Fine-Tuning Too Early
When a general-purpose LLM (or an agent built on it) doesn't fit the problem, the reflex is to fine-tune it. I think that's usually the third thing to try, not the first. There are three ways to close the gap: grounding context, harness optimization, and fine-tuning, and which one you reach for should depend on the need in front of you: domain knowledge, agent behavior, or raw performance.
Is Physical AI the Next Level for MES? I Think It Is
MES was never really about running the machines. It was about improving the operation, and it was limited by how much had to be modeled by hand and decided by people. Physical and agentic AI can change that, but only if the plant's systems are joined through shared operational context.