How Citizen Developers Changed the Game ↔ Agentic AI: Building Self-Directed Software Agents with Multimodal Reasoning

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NextGen Scientific Publication Volume 3 Issue 5, Pg. No. 14-24, AIJCST-V3I5P102, 2021 American International Journal of Computer Science and Technology https://doi.org/10.63282/3117-5481/AIJCST-V3I5P102 Copyright @ 2021 by the Author(s). This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-NC-ND 4.0) License (https://creativecommons.org/licenses/by-sa/4.0/) Original Article *Adityamallikarjunkumar Parakala1, Aaron Bell2 1Lead Rpa Developer at Department of Economic Security, USA. 2Sr. Specialist SAP OTC,Microvention Inc, USA. Article History: Received: 11.07.2021 Revised: 08.08.2021 Accepted: 01.09.2021 Published: 06.09.2021 1. Introduction The term "citizen developer" has quickly gone from being a buzzword to a major change in how businesses use technology today. A citizen developer is a business user, not a professional software engineer. They work in fields like finance, marketing, HR, or Abstract: 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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Original Article *Adityamallikarjunkumar Parakala1, Aaron Bell2 1Lead Rpa Developer at Department of Economic Security, USA. 2Sr. Specialist SAP OTC,Microvention Inc, USA. Article History: Received: 11.07.2021 Revised: 08.08.2021 Accepted: 01.09.2021 Published: 06.09.2021 1. Introduction The term "citizen developer" has quickly gone from being a buzzword to a major change in how businesses use technology today. A citizen developer is a business user, not a professional software engineer. They work in fields like finance, marketing, HR, or Abstract: 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. Keywords: Agentic Ai, Multimodal Reasoning, Autonomy, Reinforcement Learning, Symbolic Ai, Artificial Intelligence. *Adityamallikarjunkumar Parakala & Aaron Bell [2021] How Citizen Developers Changed the Game 15 operations and utilize low-code and no-code (LCNC) platforms to build applications, automate tasks, and solve business problems. These people don't write thousands of lines of code from scratch. They make good solutions for their firms by using drag-and-drop interfaces, pre-made parts & guided by their templates. These solutions are typically quite important. The citizen developer is revolutionary not just because of their technical skills, but also because they can link business needs with technology execution, which is often hard for traditional IT frameworks to do. To understand the importance of citizen development, you need to look at the history of traditional IT. For many years, only professional programmers were able to make applications. Business teams would figure out what they needed, spell out what they needed, and ask IT for it. These requests typically added to a long list of things to do, with priority based on how many resources were available, how easy it was to do the work, and how much money was available. So, it can take months or even years for a project to come up with a working solution. The corporate world may have changed by the time an application was finally put into use, making the solution less useful. Both parties were angry because the company needed things done quickly and IT was taking too long to deliver. IT staff were burdened by demands they couldn't meet quickly enough, and business units thought that their needs weren't being handled quickly enough. Three main things are closely related to the rise of citizen developers: digital transformation, a lack of IT workers, and a growing need for quick fixes.