Consequence-Aware Agentic AI: A Pre-Execution Outcome Assessment Framework for Responsible Large Language Model Agents

Large language model (LLM) agents are increasingly deployed in sequential decision-making pipelines where each action irrevocably modifies the environment. Existing agentic architectures commit to action sequences generated by the LLM without systematically evaluating the downstream consequences of those sequences prior to execution - a fundamental gap that amplifies risk and limits accountability. We introduce Consequence-Aware Agentic AI (CA2I), a formal framework that augments the standard generate-then-execute paradigm with a dedicated Consequence Assessment Module (CAM). CAM evaluates every candidate action sequence against a historical consequence model and selects the sequence whose predicted outcome optimally balances return and risk before any irreversible action is taken. We formalise the framework as a constrained sequential decision problem and prove that, under mild stationarity assumptions, Sharpe-ratio-based selection dominates random selection in expectation. To validate the framework empirically, we instantiate CA2I in the domain of LLM-driven financial trading strategy selection, using GPT-5.4 to generate five candidate strategies per asset and a backtesting engine as the consequence model. Experiments on ten diversified US equities over a strictly held-out three-month out-of-sample window (August--October~2025) demonstrate that CA2I achieves a mean test-period Sharpe ratio of 0.51 - a 4.25 times improvement over random strategy selection (Sharpe = 0.12, p = 0.041) - while reducing mean maximum drawdown relative to buy-and-hold by 18%. Sensitivity analysis confirms selection stability: Sharpe-ratio and Calmar-ratio selection agree on 100% of strategy choices across all ten assets. We further demonstrate that CA2I provides a concrete, computationally tractable pathway toward Responsible AI in agentic systems: by making consequence evaluation an explicit, auditable, and mathematically grounded architectural step, the framework operationalises transparency, accountability, and harm avoidance as mandated by the EU AI Act, NIST AI Risk Management Framework, and IEEE Ethically Aligned Design standards.