Auto Agent: A Self-Orchestrating System for Autonomous Agent Creation and Execution
DOI:
https://doi.org/10.15680/IJCTECE.2026.0903001Keywords:
Autonomous Agents, Artificial Intelligence, , Large Language Models (LLMs), Intelligent Automation, Natural Language Processing (NLP), Task Decomposition, AI-Based Execution, Tool Orchestration, Agent-Based Systems, Workflow Automation, Agentic AI, GEN AIAbstract
Auto Agent is an AI-driven autonomous execution framework designed to convert high-level natural language instructions into dynamically generated, task-specific autonomous agents capable of executing complex, multi-step workflows with minimal human intervention. The system addresses the rigidity and manual overhead of traditional rule-based and no-code automation platforms by introducing intelligent intent understanding, contextual reasoning, and adaptive execution powered by Large Language Models (LLMs). Auto Agent analyzes user input to extract goals, constraints, and dependencies, decomposes complex objectives into structured sub-tasks, and instantiates autonomous agents on demand that can reason, plan, and act independently. The framework incorporates a modular architecture consisting of intent analysis, planning and reasoning, tool selection, execution control, and feedback-driven memory, enabling reliable orchestration of APIs, services, and computational tools in real time. Through continuous validation, adaptive decision-making, and execution optimization, Auto Agent enhances robustness, scalability, and fault tolerance across diverse operational environments. By significantly reducing the need for manual workflow design and static configurations, the proposed system improves productivity, accelerates automation deployment, and establishes a scalable foundation for next-generation intelligent a
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