โก Quick Summary
The emergence of agentic artificial intelligence (Agentic AI) signifies a pivotal shift from traditional AI systems that follow fixed objectives to those that can autonomously represent, evaluate, and adapt their own goals. This paper introduces a new framework for understanding agency in AI, emphasizing its potential for goal-directed reasoning and purposive orchestration.
๐ Key Details
- ๐ Focus: Transition from fixed task specifications to self-regulating goal-directed systems.
- ๐งฉ Key Concept: Synthetic teleology – the ability of AI to generate and regulate its own goals.
- โ๏ธ Framework: Recursive loops of perception, evaluation, goal-updating, and action.
- ๐ Metrics: Teleological coherence, adaptive recovery, and reflective efficiency.
๐ Key Takeaways
- ๐ค Agentic AI represents a fundamental reconstitution of agency within computational systems.
- ๐ Recursive processes enable AI to adapt and sustain purposive activity over time.
- ๐ New metrics provide a foundation for evaluating AI’s purposiveness and effectiveness.
- ๐ Implications extend beyond technology, affecting societal and institutional frameworks.
- ๐ก Shift in Paradigm: Moving from algorithmic optimization to goal-directed reasoning.
๐ Background
The field of artificial intelligence has traditionally focused on systems that optimize predefined objectives. However, as technology evolves, there is a growing need for AI systems that can not only execute tasks but also self-evaluate and adapt their goals based on changing environments. This paper explores the concept of agentic AI, which embodies this new capability, marking a significant advancement in AI research and application.
๐๏ธ Study
The study presents a theoretical framework for understanding agentic AI, building on early agent-based process management systems. It proposes a general theory of synthetic purposiveness, where agency is viewed as a distributed and self-maintaining property of artificial systems. The authors introduce the concept of synthetic teleology, which allows AI to generate and regulate its own goals through continuous self-evaluation.
๐ Results
The paper formalizes the dynamics of agentic behavior through a recursive goal-maintenance equation. It outlines design patterns and measurable indicators of purposiveness, such as teleological coherence and adaptive recovery, providing a robust foundation for future empirical investigations into agentic behavior in AI systems.
๐ Impact and Implications
The implications of this research are profound, suggesting a paradigm shift in how we design and interact with AI systems. By reclaiming agency as a central construct, we can foster AI that not only performs tasks but also engages in goal-directed reasoning. This shift could lead to significant changes in various sectors, including technology, ethics, and governance, as we navigate the complexities of AI in open-ended environments.
๐ฎ Conclusion
This study highlights the transformative potential of agentic AI in redefining the capabilities of artificial systems. By enabling AI to engage in self-regulation and goal adaptation, we open the door to more sophisticated and responsive technologies. As we move forward, it is crucial to explore the ethical and societal implications of these advancements, ensuring that AI serves humanity’s best interests.
๐ฌ Your comments
What are your thoughts on the rise of agentic AI and its implications for the future? Let’s engage in a discussion! ๐ฌ Share your insights in the comments below or connect with us on social media:
From the logic of coordination to goal-directed reasoning: the agentic turn in artificial intelligence.
Abstract
The rise of agentic artificial intelligence (Agentic AI) marks a transition from systems that optimize externally specified objectives to systems capable of representing, evaluating, and revising their own goals. Whereas earlier AI architectures executed fixed task specifications, agentic systems maintain recursive loops of perception, evaluation, goal-updating, and action, allowing them to sustain and adapt purposive activity across temporal and organizational scales. This paper argues that Agentic AI is not an incremental extension of large language models (LLMs) or autonomous agents in the sense we know it from classical AI and multi-agent systems, but a reconstitution of agency itself within computational substrates. Building on the logic of coordination, delegation, and self-regulation developed in early agent-based process management systems, we propose a general theory of synthetic purposiveness, where agency emerges as a distributed and self-maintaining property of artificial systems operating in open-ended environments. We develop the concept of synthetic teleology-the engineered capacity of artificial systems to generate and regulate goals through ongoing self-evaluation-and we formalize its dynamics through a recursive goal-maintenance equation. We further outline design patterns, computational semantics, and measurable indicators of purposiveness (e.g., teleological coherence, adaptive recovery, and reflective efficiency), providing a foundation for the systematic design and empirical investigation of agentic behaviour. By reclaiming agency as a first-class construct in artificial intelligence, we argue for a paradigm shift from algorithmic optimization toward goal-directed reasoning and purposive orchestration-one with far-reaching epistemic, societal, and institutional consequences.
Author: [‘Haidemariam T’]
Journal: Front Artif Intell
Citation: Haidemariam T. From the logic of coordination to goal-directed reasoning: the agentic turn in artificial intelligence. From the logic of coordination to goal-directed reasoning: the agentic turn in artificial intelligence. 2025; 8:1728738. doi: 10.3389/frai.2025.1728738