Agentic AI: Building Self-Directed Software Agents with Multimodal Reasoning
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I1P101Keywords:
Agentic Ai, Multimodal Reasoning, Autonomy, Reinforcement Learning, Symbolic Ai, Artificial IntelligenceAbstract
Artificial Intelligence (AI) has come in with highly intelligent systems that continuously do ever more complex tasks. The proposed research relates to one of the newer paradigms in AI research, Agentic AI, which can be understood as autonomous, self-directed software agents that can execute goal-driven behavior via multimodal reasoning. This paper explores the design, construction, and deployment of Agentic Artificial Intelligence systems capable of synthesizing information across different modalities, including text, images, audio, and environment monitoring sensors, so as to generate intelligent autonomous choices. The main deliverable of the research is the development of a framework, which combines multimodal mechanisms of reasoning with agent-based architectures, and allows adaptive and context-sensitive behavior. To address this problem, we postulate a modular architecture that integrates the ability to learn fast and enough through reinforcement learning and profound associations throughout symbolic reasoning in this paper to effectuate decision-making in a real-time scenario and learning in a challenging arena. Our literature review is extensive and follows the development of autonomy in AI systems, the purpose of multimodal reasoning and issues in integration. The approach we use presents a layered model, which consists of perception, cognition, and action modules that accomplish specific tasks and communicate with each other using a common knowledge base. Our prototype system has been tested on various benchmarking scenarios, including navigation, task planning, and multi-agent coordination. Experience indicates a significant increase in task completion rate, awareness of context, and learning efficiency compared to unimodal and static AI agents. The paper concludes with a discussion of the ethical implications, limitations, and future trends of developing generalizable, safe, and socially agreeable autonomous agents. The study aims to develop agents that not only act intelligently but also learn and respond to new circumstances in intelligent ways
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