The rise of agentic artificial intelligence (AI) — systems capable of setting goals, creating plans, and executing multi-step tasks independently — marks a pivotal shift in technology. Unlike traditional AI that responds to prompts, agentic AI can operate autonomously, raising urgent questions about governance. According to experts at the Special Competitive Studies Project (SCSP), a nonprofit and nonpartisan initiative focused on strengthening America's long-term AI competitiveness, the pace of AI capability development is accelerating due to a self-reinforcing loop where AI helps build better AI. This could soon outpace current projections, as noted by SCSP President Ylli Bajraktari in a recent newsletter.
The implications for global security are profound. Bajraktari warned that adversaries may deploy agentic AI systems in areas with weak governance, using them for coercion, espionage, and influence. An AI agent capable of navigating complex bureaucratic systems, identifying vulnerabilities, and acting without leaving a clear attribution trail represents a qualitative leap in adversarial capability. The United States must be aware that these systems could be exploited where governance is weakest, making proactive oversight essential.
Effective governance, however, does not center on the AI model itself but on the scaffolding built around it, SCSP experts explain. This scaffolding includes connectors to bridge the model to real-world infrastructure like email and financial platforms; memory for learning and adaptation over time; planning capabilities to break down objectives and navigate obstacles; permission structures defining access; and guardrails that specify refusals, such as spending limits or human sign-offs. Without robust scaffolding, agentic AI could operate without adequate controls.
Accountability remains a major challenge, with current governance falling short in three key areas. First, responsibility is often untraceable when AI acts on behalf of a user; it is unclear who authorized specific actions. Second, existing frameworks focus on task completion rather than whether the AI performed safely or caused harm. Third, agentic AI can build detailed personal profiles by accumulating behavioral data, potentially infringing on privacy more than individuals desire.
Despite these risks, SCSP emphasizes that agentic AI should not be feared but understood and governed. Institutions that prioritize shaping and overseeing this technology will determine their competitive position and influence the global environment in which agentic AI operates. For more insights on effective governance strategies, visit scsp.ai.
