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Agentic artificial intelligence extends beyond conventional model-output generation by interpreting objectives, decomposing tasks, maintaining contextual information, selecting tools, interacting with external environments, and executing sequences of actions that may create downstream consequences. These capabilities complicate the question of who controls an agent and expose limitations in governance approaches based only on alignment, instruction following, output review, or nominal human supervision. This article develops a conceptual governance architecture for trustworthy agentic AI. The approach treats agentic AI as an autonomous decision system embedded within a wider socio-technical environment and organizes governance around explicit control points rather than a single supervisory mechanism. It distinguishes governance requirements for goal formation, goal interpretation, instruction hierarchy, task boundaries, tool access, memory retention, environmental interaction, action verification, failure containment, human approval, override capacity, responsibility allocation, auditability, and lifecycle review. The architecture proposes that consequential actions should be connected to authorized goals, bounded permissions, traceable memory and data inputs, pre-action checks, post-action evaluation, escalation pathways, and accountable human or institutional actors. It also establishes explicit sufficiency boundaries: goal alignment does not guarantee appropriate behavior; tool restrictions do not eliminate harmful use; memory controls do not guarantee reliable context; verification does not certify safe action; and human override does not independently establish meaningful control. The principal contribution is an integrated architecture-level account of how technical, human, and organizational controls may be connected across the agent lifecycle. The architecture supports conceptual analysis, governance planning, and future empirical evaluation, but does not establish legal compliance, operational safety, ethical sufficiency, institutional accountability, or deployment readiness.

Artificial intelligence increasingly contributes to decisions that shape access, opportunity, services, evaluation, and institutional treatment. Although explainability is widely treated as a foundation of trustworthy AI, an explanation does not by itself enable an affected person to question a decision, introduce corrective evidence, request reconsideration, or obtain an appropriate response. This article develops a conceptual Human Contestability Framework for connecting explanation to actionable decision challenge. The framework treats contestability as a socio-technical requirement involving affected-person access, reason-giving, explanation quality, procedural accessibility, evidence submission, meaningful human review, override capacity, escalation, appeal, redress, correction, organizational accountability, documentation, auditability, monitoring, and feedback updating. It distinguishes interpretability and transparency from contestability, and separates human review from meaningful reconsideration, appeal from recourse, and correction from broader redress. The framework proposes that contestable decision design should begin with decision-context and stakeholder mapping, define explanation and evidence requirements, establish challenge and review pathways, assign organizational responsibilities, preserve traceable records, and use contestation outcomes to identify recurring system or governance failures. Its contribution is not a validated procedure or universal institutional model, but an integrated structure for analyzing whether people can move from receiving an AI-supported decision to questioning its basis and seeking a reasoned institutional response. Contestability may strengthen trustworthy-AI planning, but it does not independently establish fairness, legality, ethical sufficiency, accountability, operational safety, or deployment readiness. Domain-specific validation, accessible procedures, competent human review, organizational governance, and continuing evaluation remain necessary.