Autonomous embodied agents must sustain a long decision-making loop of perceiving, acting, verifying, and self-correcting. In trained end-to-end policies and hand-designed workflows, the interaction structure remains externally specified. We study how much of this loop a general-purpose model can direct through a minimal interface. We term this organization agentic embodied control: the reasoning model directly steers every action, keeping reasoning and control aligned. We evaluate fourteen frontier multimodal models as provided by their vendors, with monocular RGB and primitive actions across six navigation and embodied question-answering benchmarks. On 100-episode subsets, the best agents exceed published scores and approach or surpass our human tester. Who directs the loop matters: SmartWay and O2C-Nav using the same strong models achieve substantially lower success rates on R2R-CE. The lightweight mini-swe-agent harness performs comparably to vendor harnesses. Stronger models benefit from optional rather than imposed waypoints; adding SLAM raises R2R-CE success to 88%. Physical trials expose limitations in body awareness, execution feedback, and persistent spatial understanding.

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