Number of items completed.
Tracks how many items have been successfully processed so far in the current operation. This value increments as each item is completed, providing real-time progress indication.
The ratio of completed to total gives the completion percentage:
progress = (completed / total) * 100
Timestamp when the event was created.
ISO 8601 formatted date-time string indicating when this event was emitted by the system. This timestamp is crucial for event ordering, performance analysis, and debugging the agent workflow execution timeline.
Format: "YYYY-MM-DDTHH:mm:ss.sssZ" (e.g., "2024-01-15T14:30:45.123Z")
A unique identifier for the event.
Function calling trial statistics for the operation.
Records the complete trial history of function calling attempts, tracking total executions, successful completions, consent requests, validation failures, and invalid JSON responses. These metrics reveal the reliability and quality of AI agent autonomous operation with tool usage.
Trial statistics are critical for identifying operations where agents struggle with tool interfaces, generate invalid outputs, or require multiple correction attempts through self-healing spiral loops. High failure rates indicate opportunities for system prompt optimization or tool interface improvements.
Complete list of collectors and transformers planned for generation.
Contains planning results from both REALIZE_COLLECTOR_PLAN and REALIZE_TRANSFORMER_PLAN orchestrators. Each plan entry includes:
The order may reflect generation dependencies, though the orchestrator determines actual execution order. The frontend can use this to display a checklist of items to generate and show progress as each item completes.
Iteration number of the requirements analysis this planning reflects.
Indicates which version of the requirements analysis this planning work is based on. This step number ensures that the planning decisions are aligned with the current requirements and helps track the development of implementation strategy as requirements evolve.
The step value enables proper synchronization between planning activities and the underlying requirements, ensuring that the generation plan remains relevant to the current project scope and business objectives.
Detailed token usage metrics for the operation.
Contains comprehensive token consumption data including total usage, input token breakdown with cache hit rates, and output token categorization by generation type (reasoning, predictions). This component-level tracking enables precise cost analysis and identification of operations that benefit most from prompt caching or require optimization.
Token usage directly translates to operational costs, making this metric essential for understanding the financial implications of different operation types and guiding resource allocation decisions.
Total number of items to process.
Represents the complete count of operations, files, endpoints, or other entities that need to be processed in the current workflow step. This value is typically determined at the beginning of an operation and remains constant throughout the process.
Used together with the completed field to calculate progress percentage
and estimate time to completion.
Unique identifier for the event type.
A literal string that discriminates between different event types in the AutoBE system. This field enables TypeScript's discriminated union feature, allowing type-safe event handling through switch statements or conditional checks.
Examples: "analyzeWrite", "databaseSchema", "interfaceOperation", "testScenario"
Event fired when the Realize planning phase completes collector and transformer planning.
This event occurs after the planning orchestrators (REALIZE_COLLECTOR_PLAN and REALIZE_TRANSFORMER_PLAN) have analyzed operation requirements and determined which collectors and transformers must be generated. The planning phase solves dependency resolution by identifying all required code modules before the write phase begins, enabling parallel generation and ensuring all dependencies exist.
The planning results provide visibility into the generation strategy before actual code generation starts, allowing the frontend to display what will be generated and enabling users to track progress as each planned item is realized.
Author
Samchon