Articles
Operator notes.
Problem-first writing on AI, automation, and the process underneath. Each piece names a real bottleneck, exposes the broken layer nobody owns, and shows what the working system actually looks like.
Deterministic automation vs AI agents: reasoning can be probabilistic, execution can't
Deterministic automation vs AI agents — the industry just renamed the unit of deployment from agent to harness. What that means for which parts of your workflow should never be probabilistic.
9 min read → Logistics & trade complianceAI customs entry automation breaks on a product master that stores conclusions
AI customs entry automation multiplies entries, not correctness. Two July tariff actions made the same USMCA field mean opposite things four days apart.
7 min read → Finance & procurementAgentic payments are only as safe as the supplier record underneath them
Agentic payments went live in B2B this month. The agent picks the supplier and sends the money — out of a vendor record nobody owns and nobody verifies.
7 min read → Enterprise AIDeploying an AI agent means writing down the work nobody documented
What to configure before deploying an AI agent — policies, approved actions, escalation rules. The hard part isn't the model. It's the process nobody wrote down.
7 min read → AI governanceThe EU AI Act August 2026 deadline didn't move — the one everyone staffed for did
The EU AI Act August 2026 deadline still lands: Article 50 transparency is unchanged while high-risk rules slid to December 2027. What the split actually means.
7 min read → Field serviceCustom vs. off-the-shelf scheduling software: your margin lives in the constraints the template can't express
Custom vs. off-the-shelf scheduling software is the wrong question. Write your constraints down first — most are standard, and the few that aren't decide your margin.
8 min read →