Building Graph-Based Agentic Systems: Failures, Fixes, and How the Answer Gets There — Part 2

Part 2 of "Building Graph-Based Agentic Systems" delves into the real-world implementation of a graph-based agentic AI system called LoanGuard AI, focusing on its compliance and investigation capabilities. The system is designed to enhance explainability by using a structured approach where an orchestrator routes questions to specialist agents, ensuring that the decision-making process is transparent and traceable. The article highlights the importance of maintaining clear boundaries between planning, execution, and data access to avoid failures common in AI systems, such as unclear system boundaries and prompt injection risks. Key challenges addressed include ensuring deterministic evaluations, implementing a strict workflow sequence, and managing context window sizes to prevent degraded reasoning. The system's architecture allows for comprehensive audit trails by storing reasoning as traversable subgraphs rather than mere logs, significantly aiding in compliance and regulatory investigations. Additionally, the article outlines future directions, such as integrating temporal regulation awareness and multi-jurisdictional capabilities, emphasizing that system design and traceability are crucial for moving AI systems from experimental phases to production-ready solutions in regulated environments.