AI agent for SAP support

Core idea

An AI agent for SAP support is most useful when it acts as a disciplined support layer: it retrieves relevant context, summarizes the issue, suggests diagnostic paths, prepares tickets, and routes uncertainty to humans.

The goal is not autonomous configuration change. The goal is faster, more consistent first-pass support while preserving authorization boundaries, auditability, and human accountability.

Minimum safe architecture

• Knowledge retrieval: approved runbooks, process notes, KEDB entries, public documentation, and system-specific support material where access is allowed.
• Authorization awareness: the agent should not expose data or suggest actions outside the user context.
• Structured diagnosis: the answer should separate evidence, likely cause, recommended next check, and escalation path.
• Human approval: any material process change, master-data change, financial impact, or configuration change needs controlled review.
• Traceability: responses should point to the sources used and leave an audit trail where the organization requires it.

Good use cases

Useful first use cases are ticket enrichment, incident summarization, runbook retrieval, duplicate issue detection, first-pass classification, and suggested diagnostic checklists. These are valuable because they reduce support friction without pretending the model owns the ERP decision.

Support takeaway

A credible SAP support agent should be conservative by design. It should say when it does not know, ask for missing evidence, and escalate early when the issue touches authorization, finance, compliance, master data, or configuration.