Observability Gaps in Agentic AI Decision Chains: A Systematic Review of Cybersecurity Implications

Agentic artificial intelligence (AI) systems—autonomous software entities capable of making sequential decisions across multiple services without human intervention—are increasingly deployed in production environments, including enterprise cloud platforms, financial systems, and critical infrastructure. Unlike traditional AI models that perform single-task predictions, agentic systems operate through complex decision chains involving tool invocation, multi-step reasoning, and adaptive behavior. The autonomous and often non-deterministic nature of these systems creates significant observability challenges. Observability—the ability to inspect, understand, reconstruct, and audit system behavior—is fundamental to cybersecurity incident response, regulatory compliance, and trust in automated decision-making. However, current agentic AI deployments exhibit gaps across four dimensions: logging (incomplete capture of decisions), traceability (fragmented audit trails across distributed systems), explainability (opaque reasoning processes), and integrity (inability to detect compromised agents). These observability gaps have direct cybersecurity implications. Security teams cannot reconstruct agent decision chains after incidents, compliance auditors cannot verify agent actions for regulatory requirements (GDPR, SOC2, HIPAA), and organizations cannot detect when agents have been adversarially manipulated or compromised. As agentic AI systems become more prevalent and autonomous, these gaps represent a growing threat to organizational security and regulatory compliance. Despite the criticality of this issue, the literature on observability in agentic AI systems remains scattered across multiple disciplines (AI systems, software engineering, cybersecurity, compliance). No comprehensive systematic review has synthesized the evidence on: (1) What observability mechanisms exist? (2) What gaps have been identified? (3) what cybersecurity risks these gaps create, and (4) What solutions have been proposed and validated? This systematic review addresses this gap by providing a rigorous synthesis of the current state of evidence on observability in agentic AI systems, with a specific focus on cybersecurity and compliance implications.