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Using SAP Integration Suite AI Adapter to turn integration failures into actionable insights

Introduction

Integration monitoring is an essential part of every integration landscape.

When customers have hundreds or even thousands of integrations running every day, monitoring becomes critical to identifying failed messages, understanding what happened, and taking corrective actions.

But there is an important question that goes beyond simply detecting a failure:

When an integration fails, how quickly can we understand what happened and what we should do next?

In many integration scenarios, the monitoring platform tells us that something went wrong. The next step still requires a person to open the Message Processing Log (MPL), analyze the failed step, inspect the backend response, and determine the probable root cause.

This is where I see an interesting opportunity for AI.

Instead of using AI to replace integration monitoring, we can use it to add an intelligence layer on top of the monitoring process.

In this blog, I will show a Proof of Concept using SAP Integration Suite, Cloud Integration, SAP Generative AI Hub, and the AI Adapter, where an integration failure automatically triggers an AI-based analysis that can be delivered to the team responsible for monitoring and support.

The Challenge: An Integration Error Is Often Just the Beginning

Imagine that an integration fails and the monitoring team receives:

 

 

HTTP 400 - Bad Request

Technically, this is useful information. However, from an operational perspective, the investigation has just started.

The support analyst may need to:

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 [[{“value”:”Using SAP Integration Suite AI Adapter to turn integration failures into actionable insightsIntroductionIntegration monitoring is an essential part of every integration landscape.When customers have hundreds or even thousands of integrations running every day, monitoring becomes critical to identifying failed messages, understanding what happened, and taking corrective actions.But there is an important question that goes beyond simply detecting a failure:When an integration fails, how quickly can we understand what happened and what we should do next?In many integration scenarios, the monitoring platform tells us that something went wrong. The next step still requires a person to open the Message Processing Log (MPL), analyze the failed step, inspect the backend response, and determine the probable root cause.This is where I see an interesting opportunity for AI.Instead of using AI to replace integration monitoring, we can use it to add an intelligence layer on top of the monitoring process.In this blog, I will show a Proof of Concept using SAP Integration Suite, Cloud Integration, SAP Generative AI Hub, and the AI Adapter, where an integration failure automatically triggers an AI-based analysis that can be delivered to the team responsible for monitoring and support.The Challenge: An Integration Error Is Often Just the BeginningImagine that an integration fails and the monitoring team receives:  HTTP 400 – Bad RequestTechnically, this is useful information. However, from an operational perspective, the investigation has just started.The support analyst may need to:Open the Message Processing Log.Identify the failed step.Inspect the backend response.Understand which business process was being executed.Identify the probable root cause.Determine the business impact.Decide whether the message can be safely reprocessed.When this happens occasionally, this process is manageable.When it happens hundreds of times across a large integration landscape, the amount of manual analysis can become significant.This led me to explore a simple question:What if AI could perform the first level of incident analysis when an integration fails?The ConceptThe idea is not to replace the Message Processing Log or the existing integration monitoring capabilities.Instead, the architecture adds an AI-assisted analysis layer.The traditional approach looks like this:  Integration Failure
↓
Monitoring
↓
Message Processing Log
↓
Manual Investigation
↓
Root Cause
↓
ActionThe proposed approach is:  Integration Failure
↓
Exception Subprocess
↓
Collect Error Context
↓
AI Adapter
↓
AI Incident Analysis
↓
Operational Notification
↓
Human Investigation / ActionThe AI provides the initial interpretation, while the existing monitoring capabilities remain available for detailed technical investigation.Use CaseThe use case is based on a Sales Order integration between an external application and SAP S/4HANA.The external application sends a Sales Order request to SAP S/4HANA through SAP Integration Suite, Cloud Integration.Under normal conditions, the integration processes the request and the Sales Order is created successfully.However, if an error occurs during the integration, the scenario follows a different path. Instead of simply reporting the technical failure, the integration captures the available error context and sends it to the AI Adapter for analysis.One important architectural principle here is that the AI processing only happens when an error occurs.The normal business flow does not need to invoke the AI model.This is an important point when thinking about AI in integration scenarios:AI does not necessarily need to be part of every transaction. It can be activated when intelligence is actually needed.Technology ComponentsThe solution combines several capabilities of the SAP ecosystem:    The key point is that these components are combined into an incident-analysis flow, rather than using AI as part of every normal transaction.Setting Up SAP Generative AI HubTo implement this scenario, the AI capabilities are provided through SAP Generative AI Hub.The Generative AI Hub provides a governed environment for accessing generative AI models and incorporating them into enterprise scenarios.For this example, the AI model is used to analyze integration incidents and generate a structured troubleshooting response.  The exact model configuration can vary depending on the available models and the customer’s SAP environment.For this scenario, the important concept is not the specific model itself, but the ability to expose generative AI capabilities to the integration flow in a controlled way.Configuring the AI AdapterOnce the AI environment is available, the next step is to configure the AI Adapter in SAP Cloud Integration.The AI Adapter is used to invoke the AI capabilities from within the integration flow.       In the Service Key, the endpoint is provided under serviceurls as AI_API_URL. This value is used as the API endpoint when configuring the connection to SAP AI Core.OAuth 2.0 Client CredentialsTo connect SAP AI Launchpad and SAP Integration Suite to SAP AI Core, the required authentication information is obtained from a Service Key of the SAP AI Core service instance.In SAP BTP Cockpit, navigate to the subaccount where SAP AI Core is provisioned and open the corresponding SAP AI Core service instance.From the service instance, create a new Service Key and download or copy its credentials. In this scenario, the AI Adapter is placed inside the Exception Subprocess.This is intentional.The AI service is not invoked for every successful Sales Order.It is invoked when an integration failure occurs and additional analysis is required.This creates an architecture where AI becomes an on-demand intelligence layer for integration incidents.Defining the AI PromptThe next step is defining what the AI should do with the integration error.Rather than asking a generic question such as:”What is wrong with this integration?”the prompt defines a structured troubleshooting task.For example:  You are an SAP Integration troubleshooting assistant.

Analyze the following integration incident.

SAP S/4HANA Error:
${property.error_message}

MPL ID:
${property.mpl_id}

Provide the analysis using the following structure:

1. Incident Summary
2. Probable Root Cause
3. Business Impact
4. Evidence
5. Investigation Points
6. Recommended Actions
7. Can the message be safely reprocessed?
8. Priority LevelThe structure of the prompt is important.The goal is not simply to generate a natural-language answer.The goal is to produce an output that can be used by an integration operations or support team.Step 1 — Capture the Error ContextThe first step in the Exception Subprocess is to capture the relevant error information generated by the integration.For example, the HTTP layer may provide:  Bad Request : 400 : HTTP/1.1 However, the backend response can contain much more meaningful information:  Material TG1100 is not defined for sales org. 1410,
distr. chan. 10, OrProperty ‘SoldToParty’ has invalid value ‘14100009000’ The integration can extract the relevant error information and make it available to the AI analysis.This distinction is important.A generic HTTP status tells us:Something failed.The backend error can help explain:Why it failed.This is one of the key requirements for an AI-assisted monitoring scenario:The AI needs meaningful context, not just an HTTP status code.Step 2 — AI Analysis with the AI AdapterOnce the relevant error context has been prepared, the Exception Subprocess invokes the AI Adapter.The AI receives the error information together with the instructions defined in the prompt.The goal is to transform a technical exception into a structured incident analysis.For example:  Technical Error
↓
AI Adapter
↓
Generative AI Model
↓
Structured Incident AnalysisThis is where the solution starts moving from monitoring toward intelligent operations.Step 3 — Processing the AI ResponseThe AI Adapter returns the generated incident analysis.The response can then be passed to the notification step, where it is included in the operational notification sent to the support or integration team.   The result is no longer just:  HTTP 400Instead, the support team receives a structured analysis.For example:  1. Incident Summary:
Sales Order creation failed in SAP S/4HANA.

2. Probable Root Cause:
Material TG1100 is not maintained for sales organization
1410 and distribution channel 10.

3. Business Impact:
The Sales Order could not be created.

4. Evidence:
Material = TG1100
Sales Organization = 1410
Distribution Channel = 10

5. Investigation Points:
Verify the material master and sales area data.

6. Recommended Actions:
Extend the material to the required sales area or
use a valid material.

7. Can the message be safely reprocessed?
No. The master data issue should be corrected first.

8. Priority Level:
High.The important difference is that the system has moved from:Error Detectionto:Contextual AnalysisStep 4 — Turning AI Analysis into an Operational NotificationOnce the AI has analyzed the integration error, the result can be delivered to the team responsible for monitoring and support.For this Proof of Concept, the AI-generated analysis is included in an automated notification. The important point is that the notification is not intended to replace the monitoring platform.It provides an AI-generated first analysis before the analyst starts the deeper investigation.In our example, the notification is sent by email. However, the same AI-generated analysis could also be delivered through other channels, such as Microsoft Teams, SAP Alert Notification service, or an IT service management platform.Where Could This Go Next?This approach opens several possibilities for extending AI-assisted integration monitoring and intelligent operations.Automatic Error ClassificationErrors could be classified into categories such as:AuthenticationAuthorizationConnectivityMappingTransformationMaster DataBusiness ValidationBackend ErrorsFinal ResultFinal ResultAfter the integration failure is detected and analyzed, the AI-generated incident analysis can be delivered to the team responsible for monitoring and support.In this example, the result is delivered by email. However, the same approach could be used to automatically create an incident or service ticket, send a notification through Microsoft Teams, or integrate with an IT service management platform.The important point is that the support team receives more than a technical error message. The AI provides a structured first analysis that can help the team understand the incident and decide what to investigate next.Example 1 — Invalid Material in SAP S/4HANA Example 2 — Invalid Business Partner in SAP S/4HANA     ConclusionThis could help operations teams prioritize and route incidents.When an integration fails, knowing that it failed is only the first step.The real challenge is understanding what happened, why it happened, and what should be investigated next.In this scenario, SAP Integration Suite, the AI Adapter, and SAP Generative AI Hub were combined to add an AI-assisted incident analysis layer to an existing integration monitoring process.The goal is not to replace monitoring, logs, or the expertise of integration teams. Instead, AI can help transform technical error information into a structured starting point for investigation and action.As integration landscapes continue to grow in complexity, this approach opens the door to a different way of thinking about integration operations:From detecting failures to understanding incidents.And perhaps the next step is not simply asking:”Did my integration fail?”but rather:”What happened, why did it happen, and what should I do next?” SAP Integration SuiteCloud Integration  SAP AI Core  “}]] Read More Technology Blog Posts by SAP articles 

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