STRATEGIC FORESIGHT IN AGENTIC AI SYSTEMS: EVALUATING ADVANCED REASONING AND LONG-TERM PLANNING FRAMEWORKS IN UNCERTAIN ENVIRONMENTS

This paper explores how strategic foresight can be incorporated into agentic AI systems by studying advanced reasoning, planning, and control frameworks. Mirror agents are characterised by autonomy, memory, and goal-directed reasoning that agentic AI must address in increasing long-horizon adaptability and uncertainty. Although principled reasoning techniques (e.g., Chain-of-Thought, Tree of Thoughts, and Re Act) and planning frameworks (e.g., Mu Zero, DreamerV2, and World Models) improve deliberation capabilities or predictive simulation abilities, they seem fragmented without unified organisation. Memory architectures like Mem GPT and Long Mem augment temporal knowledge, but foresight is still underexplored in testing. This paper considers governance instruments such as Constitutional AI, Iterated Amplification, and Eliciting Latent Knowledge that, to varying degrees, help ensure that foresight can be pointed to the future using combinations of reflection and methods for ensuring human-aligned value learning. Recent benchmarks such as Agent Bench, Web Arena, ALF World, and Science World showcase partial progress but emphasize the absence of foresight-specific evaluation metrics. To solve this issue, the paper suggests a Foresight Evaluation Framework (FEF) to provide an integrated method for evaluating and regulating jointly humanly agentic AI.