Self-Evolving AI Workflows: A Formalized Feedback Model for Autonomous Optimization
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I3P104Keywords:
Autonomous Systems, Feedback Optimization, Machine Learning Workflows, Performance Adaptation, Process Restructuring, Reinforcement Learning, Self-Evolving Architectures, Self-Optimization, Task Delegation, Workflow MutationAbstract
This paper presents a computational framework for the autonomous optimization of AI workflows. It formalizes a self-evolving process where AI systems dynamically restructure their opera- tional sequences in response to performance feedback and changing objectives. By modeling workflows as adaptive dependency graphs and detailing an algorithmic approach to recursive task delegation, this work provides a blueprint for continuous self-improvement in AI. This paper AI & ML (Modeling Feedback mechanism, formalizing the learning process) demonstrates how a governed feedback loop enables AI systems to autonomously learn and refine their operational dynamics
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