Observability for Delegated Execution in Agentic AI Systems

Researchers propose a new observability framework for tracking delegated execution in AI agent systems, addressing a critical gap where audit logs fail to distinguish which delegation scope authorized specific actions. The solution uses a lightweight gateway and information model to enable forensic reconstruction of agent activities across heterogeneous tools without relying on unreliable time-window correlation.

Large language model-based agentic systems present a novel observability challenge: traditional audit logs and execution traces cannot reliably attribute actions to specific delegation scopes when agents dynamically select tools and spawn sub-agents. This structural problem emerges because the same observable execution patterns can result from fundamentally different authorization contexts, making post-hoc reconstruction impossible with existing security frameworks. The research directly addresses compliance and security auditing needs in increasingly autonomous AI systems where accountability requires knowing not just what happened, but under whose authorization it occurred.

The problem compounds as AI systems grow more autonomous and distributed. When agents make independent tool selections and delegate tasks to cooperating sub-agents, execution traces become interleaved and fragmented. Current logging mechanisms track individual actions but lack semantic understanding of delegation boundaries, preventing organizations from answering critical forensic questions about authorization scope violations or privilege escalation patterns. This gap affects regulated industries and enterprises deploying agents with sensitive capabilities.

The proposed agent-aware observability substrate introduces a common information model that binds delegation context at execution time rather than attempting retroactive reconstruction. This shift from inference-based to context-capture-based attribution offers practical advantages for security teams and compliance auditors. The framework enables direct forensic queries without heuristic approximations, reducing false positives in security investigations.

Organizations deploying agent systems will increasingly demand delegation-scoped observability as agents gain access to sensitive data and critical infrastructure. This work provides foundational architecture for that capability, making it relevant to enterprises evaluating agent deployment risks and security teams building compliance frameworks around autonomous systems.

• →Standard audit logs cannot distinguish which delegation scope authorized actions in LLM-agent systems, creating a fundamental observability gap.
• →The proposed gateway-based approach captures delegation context at execution time, enabling reliable forensic attribution across heterogeneous tools.
• →Delegation-scoped reconstruction is essential for compliance in regulated industries deploying increasingly autonomous AI agents.
• →The framework addresses cross-system forensic queries without time-window correlation or heuristic inference, improving audit reliability.
• →This architecture becomes critical infrastructure for enterprise AI deployments where agent authorization accountability directly affects legal and security liability.