The Agent Stack Needs Proof, Boundaries, and Better Artifacts

AI Sandbox Escapes Aren’t an LLM Problem argues that agent escapes come from permission and pipeline failures, not merely model behavior. Outcome engineers should design production boundaries, least-privilege access, and explicit handoffs into the system—Principles 07, 10, 15.

GPT-6 Astra Solves Puzzles reports strong autonomous performance across ARC-AGI-3, Portal, and Baba Is You. The result is a reminder that capability claims need task-specific, reproducible evaluations tied to real outcomes rather than demos—Principle 16.

Why DBAs Are Right to Be Skeptical of AI — and Where They’re Wrong shows how AI can speed database diagnosis while guardrails and human approval contain risky production changes. That is the practical pattern for agent deployment: automate investigation, but gate irreversible actions with domain expertise—Principles 03, 10, 15.

commit-rewriter 0.1 cleans coding-agent cruft from Git history while retaining a timestamped recovery branch. Agents need publishable artifacts and rollback paths, not just generated output—Principles 08 and 13.

The Digital Front Door to Government Has Moved. Most Agencies Don’t Know It Yet explains that AI agents increasingly mediate how citizens find and use government information. Organizations therefore need authoritative, structured, discoverable source material that agents can verify and represent correctly—Principles 02, 06, and 13.