AI agent integration platforms for SAP and enterprise APIs describe the tooling and marketplaces that let organizations build, deploy, and operate LLM-driven agents that interact with core enterprise systems (for example SAP S/4HANA, ERP modules, and line-of-business APIs). As of 2026-05-12 this topic is timely because production deployments have moved beyond prototypes: enterprises require connectors, end-to-end observability, policy-driven governance, and private model options to embed agents into transactional workflows without compromising compliance or uptime. Three classes of tooling are central. Agent frameworks (e.g., LangChain) provide developer-facing SDKs and engineering platforms—including stateful runtimes (LangGraph), testing, and orchestration primitives—for composing, debugging, and deploying multi-step agent workflows. Enterprise agent platforms (e.g., Kore.ai) focus on no-code to pro-code authoring, multi-agent choreography, governance controls, and enterprise observability to manage lifecycle, audit trails, and access to SAP and other APIs. LLM and vector-service providers (e.g., Cohere) supply private, customizable models, embeddings, retrieval services, and secure inference that underpin retrieval-augmented generation and private data retrieval from enterprise knowledge stores. Current trends include standardized API connectors for SAP (OData/BAPI/IDoc), modular agent marketplaces and tool catalogs to reuse validated skills, stronger telemetry and explainability for agent decisions, and hybrid architectures that combine on-premise data controls with cloud-based model hosting. For enterprise adopters, the practical trade-offs are integration complexity, governance and compliance posture, and observability—areas these platforms aim to reconcile by pairing agent engineering frameworks with enterprise-grade orchestration and private model services.