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Agentic AI: Graph Engineering for Knowledge Integration
- Agentic AI leverages graph engineering to create a "living circuitry of thought,"* fusing horizontal workflows (explicit state machines for multi-step processes) and vertical knowledge (structured domain knowledge for retrieval and verification). This architecture mitigates common LLM failures like forgotten preconditions and hallucinations.
- Horizontal workflow graphs* impose causal and temporal order on reasoning by encoding reasoning/action states as nodes and permissible progressions as edges, preventing agents from skipping steps with unmet preconditions.
- Vertical knowledge graphs* ground decisions in verifiable facts by retrieving only semantically adjacent knowledge, limiting context bloat and reducing hallucination risk, contrasting with pulling entire documents into the context window.
- The LLM-Modulo pattern's generate-test-critique loop* integrates horizontal and vertical graphs: the LLM proposes, the vertical graph verifies content validity, and the horizontal graph verifies procedural legality, creating a feedback system that refines autonomy while capping context growth.
- According to additional sources, graph-based techniques in Retrieval-Augmented Generation (RAG) systems* address LLM limitations in factual accuracy and structured knowledge reasoning, using knowledge graphs (structured representation of entities and relationships) to enhance RAG components.
- Additional sources highlight MedReason,* which elicits factual medical reasoning steps in LLMs via knowledge graphs, improving accuracy and trustworthiness in medical contexts by guiding and constraining the reasoning process of LLMs.
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