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版本更新 · v2arXiv:2606.20631

Harnessing Agent Skills: Architectural Patterns and a Reference Architecture for Skill-Mediated LLM Agents

Boming Xia, Liming Zhu, Zhenchang Xing, Qinghua Lu, Dino Sejdinovic, Xiwei Xu

CONTEXT: Agent skills package reusable behavioural guidance and resources that large language model agents load or apply during a run. Within the agent harness, agent skill harnessing comprises connected architectural responsibilities that govern which skills enter a run, which actions they can cause, and how their use is recorded and informs revision. OBJECTIVES: We identify architectural patterns for agent skill harnessing and organise their responsibilities into a reference architecture that makes these connections explicit. METHODS: A multivocal literature review covered 36 practitioner systems and 51 academic papers; we consolidated practices into patterns and derived the reference architecture from patterns, source evidence, and architectural reasoning. We evaluated the reference architecture by mapping eight systems and applying it in Deep Agents and Hermes Agent. RESULTS: Ten patterns cover skill supply and activation, skill declarations and execution authority, verification and recording of skill use, and run evidence feeding repair and governed revision. The reference architecture organises their responsibilities into four layers (Supply Chain, Mediation, Execution Control, Evidence & Feedback) and two cross-cutting substrates, separating permission determination from enforcement and routing persistent revisions through acceptance. Mapping eight systems showed the first three layers broadly realised and 14 of 15 partial or unestablished ratings in Evidence \& Feedback. Applying the architecture in Deep Agents and Hermes Agent turned requirements into checks that exposed a shell route outside the file-tool rules and a preview--apply mismatch. CONCLUSION: The patterns and reference architecture support comparing implementations and turning requirements into responsibility allocations and checks.

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cs.AI · cs.LG
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02676

Does the Judge See What the Evaluation Requires? Input Assurance for LLM Agent Evaluation

Boming Xia, Qinghua Lu, Runze Li, Dino Sejdinovic, Liming Zhu

An LLM judge assesses agent steps from text packets. A renderer converts recorded runs into these packets, while software checkers test records and packets against requirements. We ask what evidence shows a packet is what the evaluation requires and propose four input-assurance obligations: required content survives rendering, checkers cover their requirements, a failed check stops later use and the judge reads the bytes last checked. In one pipeline with 30 trajectories, matching hashes, passing checkers and successful staging (copying packets for the judge) coexisted with violations of all four obligations, the last two in integration tests. The renderer omitted every structured tool call, leaving 53 of 60 judged steps blank despite matching hashes. For one judge configuration, later context raised error flags by 13 more on error than on correct steps. Restoring every call reduced this contrast to 2, a reduction of 11 (0.37 per trajectory, 95% cluster-bootstrap interval 0.17 to 0.53). In packets ending at the judged step, that configuration abstained at least once on every blank step, while the other four sometimes abstained but also flagged blank error and correct steps. Both separately written checkers accepted an unmatched tool result. In integration tests, a stub replacing the judge read a failed build's packet and a copy changed after staging. Three added controls stopped 18 of 19 constructed fault cases before the stub read any packet. The remaining stale script never ran. We turn the obligations into four tests and plan to evaluate their transfer to independent pipelines.

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cs.SE
arXiv ↗PDF ↗