Autonomous AI agents are increasingly being deployed in real-world environments to perform complex tasks such as workflow automation, decision support, intelligent monitoring, and adaptive reasoning. However, existing AI agent systems often experience execution failures due to unpredictable runtime conditions, incomplete reasoning, dynamic task dependencies, and unstable environmental inputs. These failures reduce system reliability and limit the adoption of autonomous agents in mission-critical applications. To address this limitation, this paper proposes a Failure-Resilient SelfAdaptive Agent Framework designed to detect execution failures, analyze system behavior, and dynamically adapt agent strategies during runtime. The proposed framework integrates autonomous task planning, failure prediction mechanisms, adaptive recovery strategies, persistent memory storage, and reinforcement-inspired learning techniques to improve execution stability and system robustness. The framework continuously monitors task execution states and updates agent behavior based on previous outcomes and contextual feedback. Experimental analysis demonstrates improved task completion rates, reduced execution interruption, and enhanced adaptability compared to conventional static AI agent architectures. The proposed framework provides a scalable and intelligent solution for building reliable autonomous agent systems in dynamic environments.
This project presented a Failure-Resilient SelfAdaptive Agent Framework designed to improve the reliability and adaptability of autonomous AI systems. Autonomous agents operating in dynamic environments frequently experience execution failures caused by uncertain reasoning paths, changing task requirements, and unpredictable runtime conditions. Traditional static architectures often fail to recover efficiently from such interruptions, leading to reduced system performance and increased computational overhead.
To address this problem, the proposed framework integrates autonomous task planning, runtime failure prediction, adaptive recovery mechanisms, persistent memory storage, and reinforcement-inspired learning strategies. The failure prediction module continuously analyzes execution behavior and identifies abnormal patterns during runtime. Once a failure is detected or predicted, the framework dynamically updates execution strategies using selective recovery operations instead of restarting complete workflows.
The persistent memory mechanism stores historical execution information and enables contextual retrieval of previous successful strategies. Experimental analysis demonstrates that the proposed framework improves task completion rates, execution continuity, and adaptive learning capability compared to traditional static agent systems. Overall, the proposed Failure-Resilient SelfAdaptive Agent Framework provides a scalable and intelligent solution for building reliable autonomous AI systems in dynamic real-world environments.
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How to Cite This Paper
M.Lokesh, P.Vamsi Vasanth Sai, P.Dinesh Reddy, Ms.B.Sasi (2026). Failure-Resilient Self-Adaptive Agent Framework for Autonomous AI Task Execution. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.
