• Kunal Chikte

Modern deployment environments require engineers to manually resolve configuration issues, dependency conflicts, and infrastructure failures — a process that is slow, costly, and error-prone. Existing CI/CD pipelines for automate execution but offer no autonomous recovery or learning capabilities. This paper proposes Kairo, a Self-Healing Deployment Framework that integrates confidence-based decision making, retrieval-augmented knowledge storage, internet-assisted problem resolution, and vector-based memory systems. Kairo continuously learns from deployment failures by discovering solutions from external sources, evaluating solution confidence, storing validated resolutions in a vector database, and reusing successful remediation strategies for future incidents. The framework reduces mean-time-to-recovery (MTTR), minimizes human intervention, and improves deployment reliability through adaptive, experience-driven learning.

Self-Healing Systems, DevOps, Deployment Automation, Retrieval-Augmented Generation, Vector Database, Autonomous Remediation, Artificial Intelligence

"KAIRO: A SELF-HEALING DEPLOYMENT FRAMEWORK USING CONFIDENCE-BASED DECISION MODELS AND RETRIEVAL-AUGMENTED KNOWLEDGE MEMORY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 6, page no.e485-e488, June-2026, Available :http://www.jetir.org/papers/JETIR2606458.pdf

"KAIRO: A SELF-HEALING DEPLOYMENT FRAMEWORK USING CONFIDENCE-BASED DECISION MODELS AND RETRIEVAL-AUGMENTED KNOWLEDGE MEMORY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 6, page no. ppe485-e488, June-2026, Available at : http://www.jetir.org/papers/JETIR2606458.pdf