Agentic artificial intelligence (AI) represents a significant evolution in autonomous systems, capable of operating with minimal human oversight. Unlike traditional AI that responds to prompts, agentic AI can independently set goals, create plans, and execute multi-step tasks. According to experts at the Special Competitive Studies Project (SCSP), a nonprofit focused on strengthening America's long-term AI competitiveness, this technology is beginning to help build better AI, potentially creating a self-accelerating loop that far outpaces current capability projections.
Ylli Bajraktari, president of SCSP, warned in a recent newsletter that an agent capable of navigating complex bureaucratic systems, identifying exploitable vulnerabilities, and acting without leaving a clear attribution trail represents a qualitative expansion of adversarial capability. In terms of global security, the United States must recognize that adversaries will deploy agentic AI systems in areas where governance is weakest, potentially using them for coercion, espionage, and influence.
Effective governance of agentic AI, SCSP experts explain, focuses not on the AI model itself but on the scaffolding built around it. This scaffolding includes connectors that bridge the model to real-world infrastructure such as email, booking systems, and financial platforms; memory that allows the system to learn and adapt over time; planning capabilities that break large objectives into smaller tasks; permission structures defining what the system can access; and guardrails determining what the system will refuse to do, such as spending limits or human sign-offs.
Accountability remains a major challenge, with governance falling short in three key areas: responsibility is often untraceable when using AI, making it difficult to determine who authorized an action; current frameworks do not assess whether an AI agent performed a task safely or caused harm, only that it was completed; and agentic AI builds personal profiles that may include sensitive data by accumulating patterns of behavior, preferences, and inferences.
Despite these challenges, SCSP emphasizes that agentic AI is not a technology to be feared or deferred. Institutions that prioritize understanding, shaping, and governing agentic AI will determine their own competitive position and influence the global environment in which this technology operates. For more information, visit scsp.ai.
