Duforest AI

Context Engineering for Multi-Jurisdictional Compliance

Dec 3, 2025 | Lab Notes

ABSTRACT – 

As regulatory frameworks diverge globally, static rule-based systems are failing to keep pace. This paper explores how Context Engineering allows agentic AI to dynamically adapt to local compliance requirements without the need to retrain core models. By leveraging Google Cloud’s Vertex AI and advanced vector search capabilities, organizations can inject real-time regulatory context into agentic workflows, ensuring “Sovereign Intelligence” that respects the specific laws of the jurisdiction in which it operates.

The Failure of Static Compliance

In the traditional software paradigm, compliance was hard-coded. If a bank operated in the UK and Singapore, developers wrote if-else logic to handle the distinct KYC (Know Your Customer) requirements of the FCA and MAS. This approach, while deterministic, is brittle. As regulations evolve—often weekly—the codebase becomes a tangled legacy of patches.
In the era of Large Language Models (LLMs), the temptation is to fine-tune models on legal texts. However, this is equally flawed. A model fine-tuned on 2023 GDPR data is hallucinating by 2024. Furthermore, a single model cannot simultaneously “believe” in the conflicting data sovereignty laws of the EU and the US without suffering from catastrophic forgetting or logic degradation .

The Solution: Context Engineering

Context Engineering is the discipline of structuring the information environment around an AI agent so that its reasoning is constrained and guided by external, verifiable truths rather than internal training data.
At Duforest, we implement this through a Retrieval-Augmented Generation (RAG) architecture that treats jurisdiction not as a code branch, but as a dynamic context layer.
1. The Sovereign Knowledge Graph

Instead of training a model on laws, we ingest regulatory texts into a Sovereign Knowledge Graph. Using Google Cloud Vertex AI Search, we index these documents (e.g., Guernsey’s Data Protection Law, EU AI Act) into vector embeddings.

  •  Example: When an agent processes a transaction in Guernsey, it does not rely on its training data for compliance. Instead, it queries the Knowledge Graph: “Retrieve the specific data retention rules for Guernsey fiduciary services as of Dec 2025.”
  • Benefit: The core model remains agnostic. If the law changes tomorrow, we update the document in the Knowledge Graph. The agent adapts instantly without a single line of code change or model retraining .
    2. Dynamic Context Injection

    We utilize Vertex AI’s Context Caching to inject heavy regulatory frameworks into the model’s active memory only when needed.

    • Scenario: A RuleXis agent is conducting an AML check for a client with entities in both Dubai and Jersey.
    • Process: The agent identifies the jurisdictions. It dynamically loads the “Dubai Context Pack” (DFSA rules) and the “Jersey Context Pack” (JFSC rules) into its working memory. It then cross-references the transaction against both sets of rules simultaneously.
    • Outcome: The agent identifies that the transaction is compliant in Dubai but flags a potential “tipping off” risk under Jersey law—a nuance a static model would likely miss .

    Case Study: Cross-Border Wealth Management

    A Duforest client, a multi-jurisdictional trust company, deployed RuleXis to automate their periodic reviews.

    • Challenge: Reviewers had to manually check 50+ distinct document types against the diverging laws of 4 different island jurisdictions.
    • Implementation: We engineered a “Context Router” using Google Cloud Functions. When a review is triggered, the router detects the entity’s domicile and retrieves the specific Context Definition for that jurisdiction.
    • Result: The AI agent successfully applied the correct “Economic Substance” tests for each jurisdiction with 100% accuracy, reducing review time by 85% while maintaining a complete audit trail of why it applied those specific rules .

    Conclusion

    The future of compliance is not in bigger models, but in better context. By engineering the context rather than the code, we create Sovereign Intelligence; AI that is fluid, accurate, and deeply respectful of the “Regulated World” it serves.

    References

    [1] “Context Engineering: The Secret to High-Performing Agentic AI,” Multimodal.dev, 2025.
    [2] “Compliance-Aware AI Deployment in Multi-Jurisdictional Cloud Environments,” M. Vivian, ResearchGate, 2025.
    [3] “How Strise Uses Gen AI and Vertex AI to Accelerate AML Compliance,” Google Cloud Blog, 2024.
    [4] “Designing Adaptive AI Compliance Architectures,” J. Greg, ResearchGate, 2025.