Agentic AI: Expanding the Algorithmic Frontier of Creative Problem Solving

• URL: http://arxiv.org/abs/2502.00289v1
• Date: Sat, 01 Feb 2025 03:14:59 GMT
• Title: Agentic AI: Expanding the Algorithmic Frontier of Creative Problem Solving
• Authors: Anirban Mukherjee, Hannah Hanwen Chang,
• Abstract summary: Agentic Artificial Intelligence (AI) systems are capable of autonomously pursuing goals, making decisions, and taking actions over extended periods.<n>This transition from advisory roles to proactive execution challenges existing legal, economic, and marketing frameworks.<n>We highlight gaps in liability attribution, intellectual property ownership, and informed consent when agentic AI systems enter into binding contracts or generate novel solutions.
• Score: 0.2209921757303168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Agentic Artificial Intelligence (AI) systems are capable of autonomously pursuing goals, making decisions, and taking actions over extended periods. Unlike traditional generative AI, which responds reactively to prompts, agentic AI proactively orchestrates complex workflows--as exemplified by travel-planning agents that autonomously book flights, negotiate hotel rates, curate brand-aligned experiences, and adapt to real-time disruptions. We posit that this transition from advisory roles to proactive execution challenges existing legal, economic, and marketing frameworks. We highlight gaps in liability attribution, intellectual property ownership, and informed consent when agentic AI systems enter into binding contracts or generate novel solutions. Central to this analysis is the tension between novelty and practicality: although agentic AI can craft unconventional and highly original experiences, these outputs may conflict with user preferences or logistical constraints. Furthermore, algorithmic coordination among AI systems risks distorting competitive dynamics through tacit collusion or market concentration, particularly if diverse AI systems converge on similar solutions due to shared underlying data or optimization logic. Addressing these challenges will necessitate interdisciplinary collaboration to redefine legal accountability, align AI-driven choices with consumer values, and maintain ethical safeguards. We advocate for frameworks that balance autonomy with accountability, ensuring stakeholders can harness agentic AI's potential while preserving trust, fairness, and societal welfare in an increasingly automated ecosystem.

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