Recoverable Agentic AI for Communication Networks: A Survey of Architectures, Trust Regulation, and Failure Recovery

Abstract

Agentic artificial intelligence is increasingly integrated into communication networks, enabling systems that perceive network state, reason over conditions, and execute actions on live infrastructure. As these systems act directly on operational environments, their behavior after failure remains insufficiently characterized: existing evaluation emphasizes throughput, latency, and accuracy, without capturing how systems recover after actions that affect network state. This survey examines agentic artificial intelligence in communication networks through the lens of recoverability, based on a systematically coded corpus of the recent literature. The paper introduces the MATR-R framework, which organizes recoverability around memory awareness, action governance, trust regulation, and recovery capability. At its core lies the R0–R5 graded recovery scale, which classifies post-failure capability on an ordinal scale ranging from detection to learning. The survey further presents a taxonomy of communication-network failure modes, a recovery-aware evaluation framework, and five tutorial scenarios across slicing, cybersecurity, and digital-twin-assisted recovery. The analysis reveals three consistent patterns: containment is the most common recovery level, self-healing is more often claimed than demonstrated, and full autonomy does not coincide with recovery beyond containment. These findings highlight a gap between perceived and verified recovery capability. Recoverability emerges as a distinct design dimension for agentic communication networks, with open challenges in recovery benchmarking, post-recovery verification, and standardization.