Agent demos can look reliable until real inputs trigger missing records, tool errors, policy changes, or capability limits that reveal the system is not production-ready. The article’s example billing trace includes a 13.4s run that used 2,180 tokens and cost $0.04, illustrating how a harness logs and gates work around the model. Production deployments change by treating the model as only one component and adding tool contracts, permissions, deliberate context, traces, and failure-focused tests so wrong calls are contained and diagnosable.
AI agents have caused multiple production incidents by deleting data, exposing user information, enabling account takeovers, and leaking private repositories despite performing their intended tasks. The article points to an Instagram support chatbot being used to hijack thousands of accounts. Organizations should add four safeguards—Sense, Decide, Act, and Secure—to keep agents’ inputs current, ground actions in decision history, orchestrate multi-step workflows, and restrict/monitor permissions with an emergency kill switch.
MirroS released Code-as-World, a method that converts real videos into executable MuJoCo physics programs by fitting an editable scene.json rather than predicting pixels, latents, or captions. The agentic propose→execute→verify loop recovers those programs in up to 5 rounds and Code-as-World-VL-9B scores 55.4 MRA on QuantiPhy-validation (above Gemini-3.1 Flash at 54.8). Verified worlds produced by this loop become training supervision with exact physical labels, and MirroS shipped Apache 2.0 code plus 4B and 9B checkpoints with an OpenAI-compatible /v1 vLLM endpoint for internal research use.
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