Project Phoenix: Autonomous Self-Healing via Tree-of-Thoughts Deliberation and Self-Refine Iterative Correction

Abstract

Modern distributed software systems fail in ways that static recovery policies cannot anticipate and that human operators cannot address at production speed. Rule-based autonomic systems are constrained to pre-defined failure modes, while learning-driven agents risk uncontrolled behavior during exploration in live environments. This paper presents Project Phoenix, an autonomous self-healing framework that addresses this challenge through a closed-loop architecture integrating Tree-of-Thoughts (ToT) deliberation for structured pre-execution multi-path recovery planning with Self-Refine iterative correction for post-execution policy refinement. The framework spans continuous health monitoring, probabilistic failure detection, multi-strategy autonomous decision-making, coordinated multi-agent recovery execution, iterative self-correction, and reinforcement-learning-based policy adaptation. Internal validation across 3,416 automated test cases demonstrated implementation consistency across all tested scenario families; external benchmarking against independent workloads is identified as future work.

Introduction

The framework consists of seven modules: Health Monitor, Failure Detector, Decision Engine, Recovery Orchestrator, Self-Correction Engine, Continuous Learning System, and Memory Manager. Key features include:

• Health Monitor & Failure Detector: Continuous monitoring and multi-stage anomaly detection with structured incident reporting.
• Decision Engine & Recovery Orchestrator: Multi-strategy plan selection and safe execution with rollback and fallback mechanisms.
• Self-Correction & Continuous Learning: Post-execution critique feeds Q-learning policy updates, improving future recovery outcomes and minimizing MTTR.
• Memory Manager: Stores operational context and longitudinal patterns to inform future decisions.

By integrating deterministic execution, pre-execution deliberation, post-execution adaptation, and multi-tier memory, Project Phoenix balances safety, responsiveness, and adaptability, providing a scalable, autonomous solution for managing complex failures in distributed systems.

Conclusion

This paper presented Project Phoenix, an autonomous self-healing framework integrating Tree-of-Thoughts-inspired pre-execution deliberation with Self-Refine-inspired post-execution correction into a single closed-loop system. The framework addresses a gap in existing approaches: the absence of a system that simultaneously provides deterministic safety, structured deliberation before commitment, iterative self-correction after execution, multi-tier longitudinal memory, and continuous RL-based adaptation. Internal validation across 3,416 automated test cases demonstrated internal consistency across all seven modules and five scenario families within the controlled test environment. These results establish a foundation for the external benchmark evaluation described in Section IX-B. A rigorous publication-grade benchmark protocol has been specified to establish external performance evidence through controlled experiments against three baseline systems, with primary metrics of RSR, MTTR, and Adaptation Gain reported at the 95% confidence level. If validated under real production workloads, a framework of this kind could meaningfully reduce the operational burden on engineering teams managing complex distributed systems. Future work will focus on three directions: (1) live production deployment to build the external benchmark dataset; (2) integration of LLM inference into the Decision Engine’s plan evaluation step to augment structured metric signals; and (3) extension to federated multi-cluster environments where recovery coordination spans heterogeneous infrastructure stacks.

References

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Copyright

Copyright © 2026 Dr. Pendela Srinivasarao, Shaik Balaji Mahammad Rafi, Shaik Rasheed, Thatikonda Devi Sri Chaitanya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.