In the 2024-2025 robotics competition season, while writing autonomous navigation scripts, a pattern emerged: the robot already had everything an AI agent needs but lacks -- compiled knowledge, operational learning, failure recovery, persistent identity, and a communication protocol. AETHER applies this pattern to software agent design. This paper presents AETHER (Adaptive Embodied Thinking -- Holistic Evolutionary Runtime), a minimal agent framework built on two architectural processes: DAG (Distilled Augmented Generation) for agent creation, DAG(S, K, P) -> C; and DAGR (Distillation + Augment + Generation + Retrieval) for runtime execution, DAGR(Q) = R(G(A(D(Q), KG, KB), P), KG_compiled). The result is an agent that is self-educating, self-healing, grounded, and portable -- a skill that travels intact across any LLM. DAGR's Retrieval stage implements Agent Education Calibration (AEC), a verification engine that compiles typed JSON-LD knowledge graph nodes into executable policy checkers at capsule load time in O(|N|). At runtime, AEC verifies each statement in O(|tokens|) via set intersection -- no embeddings, no vector databases, no GPU. Measured verification: 0.3-0.8ms per statement on standard consumer hardware. When verification fails, a self-education loop identifies knowledge gaps, researches missing content via LLM, validates proposed knowledge through AEC, and enforces a contradiction gate where immutable core nodes hold absolute veto over acquired knowledge. Implemented in 15 Python files (stdlib only), 33 capsules across six categories, fully reproducible. Evaluation: AEC scores of 1.0, 0.6, and 0.14 across three coverage scenarios; 0.857 on a 73-node design agent with real rule compliance and anti-pattern detection; autonomous score improvement from 0.143 to 0.889 with 17 triples acquired, zero human intervention. The model did not change. The skill did. Repository: github.com/jeff0926/aether