Agentic systems—artificial intelligence systems capable of multi-step reasoning, tool orchestration, contextual memory, and iterative execution—are increasingly used in dynamic workflows that extend well beyond one-shot prediction or retrieval. These systems offer substantial capability gains, but they also introduce a qualitatively distinct class of reliability failures. This paper identifies the dominant failure condition in such systems as confident misalignment, in which outputs are coherent, plausible, and authoritative while nevertheless diverging from validated reality or from the user's actual intent. Unlike conventional failures, these errors often remain silent, preserving the appearance of correctness while shifting the burden of detection onto the human operator. This paper argues that the principal limitation of agentic systems is not insufficient intelligence, but the absence of structured alignment across governance, context, collaboration, and execution. Drawing on AI governance, human–AI interaction, human factors, public-sector AI research, neuroplasticity literature, and public guidance from NIST and the U.S. Department of Defense, it develops a structured problem set centered on context integrity failure, communication asymmetry, silent degradation, execution–design divergence, and trust erosion. It then introduces the HGC³AE² framework—Human-governed, Curated Context, Agentically Engineered and Executed—as a governance-first architecture for maintaining alignment between human authority, contextual truth, and runtime behavior. The framework is operationalized through the conceptual introduction of the Skipjack Protocol, which enforces live validation, prevents silent fallback, requires explicit uncertainty signaling, and structures reconciliation as a continuous process. The central claim of this paper is that scalable and trustworthy agentic deployment depends less on maximizing autonomy than on embedding enforceable mechanisms that keep human intent, contextual truth, and system execution synchronized over time. Rights envelope: Citation permitted with full attribution. No reproduction, redistribution, or derivative works without written permission. AI/ML training use disallowed. See the citation policy at https://nonsequitur.tech/pubs/citation-policy/ for the full rights envelope. Canonical site URL: https://nonsequitur.tech/white-papers/hgc3ae2/ Public archive: yks-pubs/papers/hgc3ae2-v1-preprint.pdf