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

• URL: http://arxiv.org/abs/2603.18096v1
• Date: Wed, 18 Mar 2026 10:23:48 GMT
• Title: A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance
• Abstract summary: This paper presents an assurance framework for Agentic AI systems using Large Language Models (LLMs)<n>Executions are instrumented as Message-Action Traces (MAT) with explicit step and trace contracts.<n>The framework includes stress testing, formulated as a budgeted counterexample search over bounded perturbations.
• Score: 0.22940141855172028
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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