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Autonomous LLM Agents Placed Inside Temporal Sagas for Core Banking Decisions

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Core banking platforms are beginning to use generative AI and large language model agents for complex tasks such as reviewing unstructured trade finance documents, triaging fraud cases, and handling cross-border exceptions. This creates a conflict, because Temporal workflows require deterministic replay to stay fault tolerant, while LLM outputs vary with temperature, hardware, and model versions. The proposed Xenon Architecture Standards resolve this by isolating LLM agents within Temporal Activities governed by strict JSON schemas, validation guardrails, and deterministic fallbacks. The paper also maps which banking domains suit varying levels of AI autonomy, keeping payments and ledger posting fully deterministic, and examines reliability models and compensation patterns.

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