According to DeepLearning.AI, SAP's Christoph Meyer and Lars Heling said enterprise AI agents often fail because they choose the wrong API and lack business process context (source: DeepLearning.AI). They emphasized that APIs execute in a discrete, ordered sequence over time, meaning agents must understand orchestration rather than isolated endpoints (source: DeepLearning.AI). They explained that knowledge graphs resolve this by defining semantics via ontologies, modeling resources, APIs, and business processes as connected nodes to guide correct execution (source: DeepLearning.AI). For traders tracking AI infrastructure, this positions ontology-driven knowledge graphs and API orchestration inside SAP-style stacks as key enablers of enterprise deployment readiness, and the session included no mention of cryptocurrencies or tokens (source: DeepLearning.AI).

According to @AndrewYNg, a new short course titled Agentic Knowledge Graph Construction shows how a team of agents can extract and connect reference materials into a knowledge graph to build better RAG. Source: Andrew Ng on X, Aug 27, 2025, https://twitter.com/AndrewYNg/status/1960731961494004077 The course is taught by Neo4j Innovation Lead @akollegger, highlighting a practical graph-database approach for RAG pipelines. Source: Andrew Ng on X, Aug 27, 2025, https://twitter.com/AndrewYNg/status/1960731961494004077 Ng emphasizes that knowledge graphs are an important way to improve RAG quality. Source: Andrew Ng on X, Aug 27, 2025, https://twitter.com/AndrewYNg/status/1960731961494004077 For traders, the announcement contains no references to cryptocurrencies, tokens, or pricing, indicating no direct, immediate crypto-market catalyst from this post. Source: Andrew Ng on X, Aug 27, 2025, https://twitter.com/AndrewYNg/status/1960731961494004077