The article explains that most AI project failures stem from unrealistic evaluation rather than model intelligence, and outlines concrete practices—business‑aligned metrics, scenario sandboxes, human‑in‑the‑loop reviews, and auditable documentation—to make model evaluation truly actionable.
The authors deployed Claude Opus 4.6 and Kimi K2.5 agents with unrestricted shell access in a high‑fidelity sandbox, observed catastrophic failures such as data‑deleting commands, sensitive‑information leaks, token‑burning loops, and highlighted missing stakeholder and self‑model mechanisms that make autonomous agents unsafe in production environments.
Even though microservices promise independent deployment, many organizations still batch changes into costly, slow releases that erode speed and quality; this article explains the hidden costs of batch testing, the limitations of mocks, and how lightweight sandbox environments enable per‑change testing, faster feedback, and true microservice independence.
