What the data for this category looks like
Daily documents for level content creation include level design drafts, resource requirement lists, test feedback sheets, and version submission records. Data sources include level editors, art resource libraries, player test data, and defect systems. Data updates are performed weekly or per version. Core fields include level ID, resource path, test node number, defect severity, and repair status. Data scale changes dynamically based on the size of the level design.
What constraints these characteristics impose on the "workflow orchestration" link
Data sources are scattered across level editors, art resource libraries, player test systems, and defect management platforms. Workflows must support pulling and integrating data from multiple sources. Data updates occur weekly or per version. Execution logic triggered by version submissions must be supported to avoid unnecessary repeated runs. Core fields change dynamically with level design. Configurable variables must be used to pass parameters. The most time-consuming playability testing phase relies on a multi-node connected test process. Defect capture and repair logic must be embedded into corresponding workflow nodes. The configuration must also adapt to the size limits of the resource library cache.
How to configure settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Mode | Triggered by version submission tags | Matches weekly or per-version update cycles |
| Retry Count | 2 times | Handles temporary network fluctuations during single tool calls |
variables configuration | Pass level ID and defect severity fields | Adapts to the core field structure of daily documents |
| Node Timeout Period | 600 seconds | Covers the time required for level resource loading and test data pulling |
| Single Recall Result Count | Top 5 entries | Matches the typical number of feedback entries on test feedback sheets |
| Resource Library Cache Size | 1000 MB | Adapts to the art resource scale of current levels |
nodeId tagging | Assign a fixed nodeId to each test phase | Facilitates subsequent traceability of defect data links |
Three common misconfigurations
- Tool call modules in workflows return empty results and do not trigger tool calls. The cause is that the exclusive data source for the corresponding level is not bound, so valid call content cannot be matched.
- Code execution modules throw errors indicating missing imported dependency libraries. The cause is that the corresponding library files are not installed in the system dependency directory of the FastGPT container.
- Workflow execution failures do not generate corresponding defect records. The cause is that no error capture node is configured, and error log fields for defect archiving are not passed via
variables.
How to verify a correct configuration
- Manually trigger the workflow, check if test feedback sheets and resource library data for the corresponding level can be pulled.
- Execute the code execution module, verify that imported dependency libraries load normally without errors.
- Simulate node execution failure, check if the error capture mechanism is triggered and corresponding defect records are generated.
- Check the
nodeIdtagging, confirm that the node ID of each test phase matches the design documents.
The values provided are common starting points and should be measured against the reader's own 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.