What data for this category looks like
Data for this category comes from incident timelines, operation logs, monitoring metrics, and code commit records. The update frequency is either after each major incident or once per month. Daily documents produced include RCA reports, incident tickets, review minutes, and rectification tracking sheets. Data fields include incident occurrence timestamp, root cause classification tags, associated test case ID, rectification item number, and more. Time-related fields are recorded in seconds. Rectification tracking items update their closure status in days.
What constraints these characteristics impose on workflow orchestration
Data sources for this category are scattered and varied. Workflows must support parallel pulling of information from different data sources. Each data source node must be configured with a unique identifier. Update frequency is low, so workflows do not need high-frequency triggering. They can be set to start on demand or trigger based on incident events. Document structures are inconsistent. Workflows must support custom field mapping to adapt to format differences across documents. The most time-consuming evidence collection and root cause verification steps require workflows to set up validation and waiting logic between nodes to prevent process interruptions. The rectification tracking closure requirement means workflows must bind status update nodes to ensure rectification item progress can be tracked.
How to configure settings
The following configurations are compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Map fields from RCA reports and incident tickets to custom variables, including fault time, root cause type, associated case ID | Adapt to the field structure of multiple types of documents for this category, unify workflow input and output formats |
nodeId | Configure a unique nodeId for log pulling, monitoring metric pulling, and code commit pulling respectively | Distinguish between multiple data source nodes to avoid node conflicts during workflow execution |
| Incident classification model | Use the built-in classification model, with the prompt limited to three categories: "fault root cause", "test defect", "environmental issue" | Align with the root cause analysis scenario for this category, reduce ambiguity in model recognition |
| MCP plugin integration | Configure access via external call nodes, set a timeout of 300 seconds | Adapt to the latency characteristics of data pulling for this category, prevent workflow interruptions due to request timeouts |
| Code execution node | Configure to trigger only during the root cause hypothesis verification phase, set a single run timeout of 60 seconds | Focus on code debugging needs for root cause verification, avoid invalid runs consuming resources |
| Failure handling strategy | Set 2 retries with a 60-second interval, trigger an alert node after failure | Reduce the rate of workflow failures caused by temporary data source fluctuations, notify relevant personnel in a timely manner |
Three common configuration errors
- The incident classification node in the workflow returns no results. This occurs because the classification scope of the prompt is not limited, leading to ambiguous model recognition boundaries that cannot match the preset root cause types.
- Workflow execution fails after MCP plugin integration. This occurs because the correct external call timeout parameter is not configured, causing plugin requests to exceed the preset duration without receiving a response.
- The code execution node does not take effect continuously. This occurs because the dependency environment configuration for code execution is not specified, leading to missing execution environments that cannot complete code compilation and operation.
How to verify proper configuration
- The workflow
variablesconfiguration page can be reviewed to confirm that fields from all associated documents have been correctly mapped to custom variables. - A test run of the workflow can be triggered, and the data source nodes corresponding to each
nodeIdcan be checked to confirm they can normally pull data of the corresponding type. - A simulated incident description can be input into the incident classification node, and the classification results can be verified to fall within the preset three categories.
- A plugin call failure scenario can be simulated, and the failure handling strategy can be verified to trigger the specified number of retries, and trigger an alert after retries fail.
The values provided here are common starting points, and should be evaluated against available samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.