Benchmarking AI Agent Recovery Under Partial Failures and Stale State
Keywords:
AI agents, partial failures, stale state, adaptive recovery, state verification, task completion, recovery benchmarking, fault tolerance.Abstract
More and more often, AI agents conduct complicated activities that involve using various external components such as APIs, memory, and the like. Yet, incomplete failures and state staleness might prevent the proper execution of tasks by AI agents and generate inconsistent decisions. For this reason, the present research suggests a way of benchmarking an AI agent's performance with respect to its recovery abilities in case of failures. Three different approaches to recovering from a failure are suggested for evaluation: baseline execution, state-aware recovery, and adaptive recovery. The new method involves performing 300 simulation cases, where 100 simulations are associated with each strategy. From the results shown above, it is clear that adaptive recovery attains a success rate of 90% in executing tasks, whereas state-aware recovery and baseline execution attain a success rate of 79% and 62% respectively. Furthermore, adaptive recovery attains 94% success rate in detecting stale states, recovery time averaging to 11.6 seconds and duplicate actions 4%. This is an indication that incorporation of state verification, bounded retries, checkpointing, and task replanning could help increase the reliability of the recovery process as well as efficiency of task execution. However, the results provided here are just examples and not real experimental measurements. Validation through real AI agents, failure injections that can be reproduced, and multiple trials ausing statistics would be beneficial for future research. Testing of computational overhead, false alarms, and performance on various tasks would add more credibility to creating resilient AI agents.
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Alsalem, M. A., Zaidan, A. A., Zaidan, B. B., Hashim, M., Albahri, O. S., Albahri, A. S., ... & Mohammed, K. I. (2018). Systematic review of an automated multiclass detection and classification system for acute Leukaemia in terms of evaluation and benchmarking, open challenges, issues and methodological aspects. Journal of medical systems, 42(11), 204. https://www.academia.edu/download/93657491/s10916-018-1064-920221104-1-ruqu9f.pdf
Bartolini, A., Beneventi, F., Borghesi, A., Cesarini, D., Libri, A., Benini, L., & Cavazzoni, C. (2019, August). Paving the way toward energy-aware and automated datacentre. In Workshop Proceedings of the 48th International Conference on Parallel Processing (pp. 1-8). https://dl.acm.org/doi/pdf/10.1145/3339186.3339215
Huang, L., Zhang, C., & Zhang, H. (2020). Self-adaptive training: beyond empirical risk minimization. Advances in neural information processing systems, 33, 19365-19376. https://proceedings.neurips.cc/paper_files/paper/2020/hash/e0ab531ec312161511493b002f9be2ee-Abstract.html
Hutchinson, B., Rostamzadeh, N., Greer, C., Heller, K., & Prabhakaran, V. (2022, June). Evaluation gaps in machine learning practice. In Proceedings of the 2022 ACM conference on fairness, accountability, and transparency (pp. 1859-1876). https://dl.acm.org/doi/pdf/10.1145/3531146.3533233
Rengarajan, A. (2022). Developing AI Powered Enterprise Integration Platforms Through Cloud Native Observability and Automated Governance. International Journal of Science, Research and Technology, 5(6), 8970-8977. http://www.ijsrat.com/index.php/ijsrat/article/download/216/208
Singh, N., Saladi, S. P., & Kumar, M. (2022). Automated Validation of Transaction Consistency and Recovery in Distributed Payment Systems: Duplicate Transactions, Retries, Timeouts, and Recovery. INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, 19-33. http://www.xlescience.org/index.php/IJASIS/article/view/2187
Yadav, D., Jain, R., Agrawal, H., Chattopadhyay, P., Singh, T., Jain, A., ... & Batra, D. (2019). Evalai: Towards better evaluation systems for ai agents. arXiv preprint arXiv:1902.03570. https://arxiv.org/pdf/1902.03570
Zhang, Q., Schulze, M., Gazdzicki, P., & Friedrich, K. A. (2021). Comparison of different performance recovery procedures for polymer electrolyte membrane fuel cells. Applied Energy, 302, 117490. https://www.sciencedirect.com/science/article/pii/S0306261921008771
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