A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance

2026-03-18

AI Summary

This work addresses the reliability and safety challenges faced by agentic AI systems during long-horizon interactions—specifically non-termination, role drift, misinformation propagation, and external attacks—by proposing a unified assurance framework based on Message-Action Trajectories (MAT). The framework enables machine-checkable verification, fault localization, and deterministic replay through formalized steps and trajectory contracts. It integrates perturbation-based stress testing, boundary fault injection, and runtime governance mechanisms that map linguistic outputs to actionable constraints, thereby unifying contract verification, structured testing, and dynamic control within multi-agent LLM orchestration systems for the first time. The study introduces trajectory-level evaluation metrics—including task success rate, termination reliability, and contract compliance—to support reproducible benchmarking across models, random seeds, and system configurations.

Technology Category

• Multiagent Systems: Agent Communication
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In Agentic AI, Large Language Models (LLMs) are increasingly used in the orchestration layer to coordinate multiple agents and to interact with external services, retrieval components, and shared memory. In this setting, failures are not limited to incorrect final outputs. They also arise from long-horizon interaction, stochastic decisions, and external side effects (such as API calls, database writes, and message sends). Common failures include non-termination, role drift, propagation of unsupported claims, and attacks via untrusted context or external channels. This paper presents an assurance framework for such Agentic AI systems. Executions are instrumented as Message-Action Traces (MAT) with explicit step and trace contracts. Contracts provide machine-checkable verdicts, localize the first violating step, and support deterministic replay. The framework includes stress testing, formulated as a budgeted counterexample search over bounded perturbations. It also supports structured fault injection at service, retrieval, and memory boundaries to assess containment under realistic operational faults and degraded conditions. Finally, governance is treated as a runtime component, enforcing per-agent capability limits and action mediation (allow, rewrite, block) at the language-to-action boundary. To support comparative evaluations across stochastic seeds, models, and orchestration configurations, the paper defines trace-based metrics for task success, termination reliability, contract compliance, factuality indicators, containment rate, and governance outcome distributions. More broadly, the framework is intended as a common abstraction to support testing and evaluation of multi-agent LLM systems, and to facilitate reproducible comparison across orchestration designs and configurations.

Problem

• Agentic AI
• LLM orchestration
• failure modes
• external side effects
• non-termination

Innovation

• Message-Action Traces
• Step Contracts
• Stress Testing
• Fault Injection
• Runtime Governance