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AgentTrustCI: Build-Time Policy, Grounding, and Trajectory Tests for Java LLM Agents

AgentTrustCI is a Java/JUnit 5 framework that adds application-layer behavioral checks for LLM agents, covering tool selection, answer grounding, and workflow trajectories via recorded traces. It uses deterministic replay for repeatable build-time tests and an optional judge interface for semantic evaluation. In a synthetic corpus of 55 cases across seven enterprise domains, the full pipeline correctly classified all 30 injected faults and 25 passing controls, including 10 non-trivial controls, with no missed critical cases. The paper emphasizes that results reflect the authored regression suite, not production accuracy, and describes a production-trace validation protocol.

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Provides a concrete, practical framework for integrating LLM agent behavioral contracts into Java CI pipelines, with reproducible test results and clear scope limitations.

arXiv:2611.06674v1 Announce Type: cross Abstract: Infrastructure security does not establish whether an LLM agent selected an allowed tool, grounded its answer in tool output, or followed a required workflow. AgentTrustCI is a Java/JUnit 5 framework that expresses these application-layer expectations as policy, grounding, and trajectory checks over recorded traces. A deterministic replay implementation supports repeatable build-time tests; a judge interface provides an extension point for optional semantic evaluation. On 55 synthetic cases spanning seven enterprise domains, the full pipeline correctly classifies all 30 injected faults and 25 passing controls, including ten non-trivial controls, and misses none of 12 designated critical cases. Each single-layer configuration correctly classifies 35 cases because the corpus deliberately isolates ten faults per layer. A grounded answer obtained through a disallowed sequence illustrates the additional coverage of trajectory checks. Repeated measurements quantify validator execution separately from process startup and reporting. These results establish coverage of the authored regression suite, not production accuracy. We describe a production-trace validation protocol, optional LLM-judge integration, and maintenance of expectations as workflows evolve. The contribution is a practical pattern for expressing behavioral contracts within an existing Java release pipeline.
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