Briefing

Agents that do the work, inside the guardrails.

A realistic reference design for AI agents that automate a real workflow across SAP, Oracle and Microsoft Azure, with security and governance for the data and for the agents themselves.

A vendor-neutral starting point, not a client's environment. Agents propose and people approve until the numbers say otherwise.

Invoice exceptions across SAP, Oracle and Azure

A manufacturer pays suppliers from SAP S/4HANA at headquarters, while a plant it acquired still records receipts in Oracle JD Edwards. When the invoice, the purchase order and the receipt don't agree, someone spends days in email. Here, agents do that work.

Explore a layer
Follow one invoice
Agentic AI reference architectureData from SAP S/4HANA, Oracle JD Edwards and Microsoft Azure flows through integration pipelines into a governed data platform. An orchestrator agent coordinates match, research, resolve and act agents. A human approval gate controls writes back to the systems. Outputs are process automation, an intelligence dashboard and plain-language questions and answers. A governance column spans the data and the agents.SAP S/4HANAPOs, vendors, payablesOracle JD Edwardsreceipts, acquired plantMicrosoft Azureportal, invoices, contractsIntegration pipelinesSAP-supported APIs and replication, JD Edwards Orchestrator and CDC, Azure-native pipelinesGoverned data platformLakehouse with catalog, lineage, sensitivity labels and a vector index for documentsAgent layertools via MCPOrchestrator agentplans, routes, tracks stateMatch agentthree-way matchResearch agentcontracts and emailResolve agentproposes the fixAct agentposts approved actionsHuman approval gate: writes above policy limits wait for a personProcess automationpostings and holds in SAP, JDEIntelligence dashboardexceptions, cash at riskAsk in plain languageQ&A over governed dataGovernancedata and agentsAgent identitiesLeast privilegeAI gatewayPII maskingRow-level accessLineageHuman approvalsAudit to SIEMEvals and driftCost limits

Swipe sideways to see the whole diagram.

How autonomy grows

Agents earn trust the way new hires do: on evidence. Each phase widens what they're allowed to do only when the numbers support it.

  1. Observe

    Agents read the data, flag exceptions and draft explanations. Nothing is written back. We measure their accuracy against what your team actually did.

  2. Recommend

    Agents propose fixes with the evidence attached, and people approve every action. Approval and override rates show where agents can be trusted.

  3. Act within limits

    Agents carry out approved action types under set dollar and risk limits. Everything else still goes to a person, and limits rise only when the numbers support it.

What we measure after go-live

Agents join the same day-two scorecard as every other provider.

Touchless rateShare of invoices that need no human touch.
Exception cycle timeHours from exception to resolution, not days.
Acceptance rateHow often people approve the agent's recommendation as proposed.
Override reasonsWhy people said no, fed back to improve the agents.
Cost per invoiceIncluding AI usage, tracked with FinOps.
Audit completenessEvery prompt, tool call and decision accounted for.

Where else this pattern pays off

Any process that runs on email, spreadsheets and manual checks between systems.

Order-to-cash credit holdsMonth-end reconciliationsSupplier risk monitoringInventory shortage alertsIT service desk triageContract renewal reviews

Have a process that runs on email and spreadsheets?

That's usually where agents pay off first. Start with a free discovery call. General inquiries: info@netvarista.com

Book a discovery call