What this category’s data looks like
This category serves customer service, technical support, and customer success teams. Data is sourced from ticketing systems, chat logs, product logs, and customer contract SLA documents. Daily maintained documents include customer tickets, issue tiering tables, processing records, and service reports. Data updates occur daily or in real time. Core fields include ticket unique identifier, customer associated information, issue description text, tiering tags, responsible team, SLA fulfillment time limit, and processing status. Data follows the flow of customer-reported issues, with traceable operation records retained across the full lifecycle from submission to archiving.
Constraints for Scheduled Tasks and Automation
Data sources are scattered and update rhythms vary. Systems must adapt to pull frequencies of different data sources to avoid repeated pulls or missed real-time submitted tickets. Core fields include tiering tags and SLA time limits. Scheduled tasks must accurately match field formats, otherwise tiering results will be invalid or SLA statistics will be incorrect. Ticket data volume fluctuates with business activity. Automated tasks must reserve sufficient processing bandwidth to prevent task backlogs during peak hours. Customer contract SLA constraints must be strictly followed: task triggering and execution times must not exceed agreed response time limits, otherwise breach risks will be triggered.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Task Trigger Cycle | Adjust based on business scenario, options are 15 minutes, 1 hour, 1 day | Matches data update rhythm. Use 15 minutes for real-time ticket scenarios, 1 day for daily summary reports. Designed based on FastGPT v4.17.0 task scheduling logic |
| Single Ticket Pull Limit | Top 1000 entries | Avoids excessive load on a single task, adapts to concurrent processing capabilities of most servers, prevents task timeouts |
| Tiering Result Verification Switch | Enabled | Ensures automatic tiering results comply with preset issue tiering table rules, filters abnormal or non-business-compliant tiering tags |
| Task Failure Retry Count | 3 times | Covers temporary network fluctuations or short system outages, reduces the frequency of manual intervention |
| Log Retention Period | 30 days | Meets compliance review requirements for operation reports, while controlling storage resource usage |
| Task Execution Time Window | Non-business peak hours | Avoids peaks of ticket submission and processing, reduces system resource conflicts, improves task execution stability |
Three Common Misconfigurations
- Phenomenon: An API call to create an issue classification workflow returns abnormal parameters or execution results that do not meet expectations. Cause: The model type corresponding to the task is not explicitly specified, and the configuration logic of the issue classification model is confused with that of other models.
- Phenomenon: No operation report is generated after a scheduled task runs, or the report data is incomplete. Cause: The task trigger cycle does not match the data update rhythm, so complete ticket and tiering data was not pulled.
- Phenomenon: Output containing Human <Instruction> content appears during tool calls. Cause: No context filtering rules for tool calls are configured, causing the system to expose internal debug instruction text.
How to Verify a Successful Configuration
- Manually trigger a scheduled task, and check the task logs for records of successful ticket pulling, tiering completion, and report generation.
- Review the generated operation report to confirm that core fields (ticket ID, tiering tags, SLA time limits) are correctly populated.
- Simulate a temporary network disconnection anomaly, and confirm that the task automatically retries according to the configured retry count.
- Compare with the preset issue tiering table, randomly select a sample of tickets’ automatic tiering results, and confirm they match the preset rules.
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.