Summary of preliminary data acquired by the agent during RAG.
Contains lightweight identifiers for each kind of preliminary data that was
loaded into the agent's local context before producing its output. Only the
kinds specified by the Kind type parameter are present.
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")
Schema definition containing the single target table model and any newly discovered table designs.
Carries exactly one model (the target table) produced by a single schema generation call so that the AI output stays within the LLM's maximum output token limit. Additional child tables needed for normalization are declared in AutoBeDatabaseSchemaDefinition.newDesigns as lightweight name + description pairs and are generated by subsequent pipeline calls.
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.
Business domain namespace where this database table belongs.
Identifies the logical business domain or functional area that this database table is part of. The namespace follows domain-driven design principles, grouping related tables together to maintain coherent schema organization and clear separation of concerns across different business areas.
The namespace determines which Prisma schema file this table will be written to, enabling systematic development and maintainable database architecture. Each namespace typically corresponds to a major business domain such as "Actors", "Sales", or "Systematic".
Strategic database design analysis and planning phase for the target table.
Contains the AI agent's analysis of the target table being designed, including its structure, relationships with other tables, normalization requirements, and performance considerations.
Iteration number of the requirements analysis this schema was generated for.
Tracks which version of the business requirements this database schema reflects, ensuring alignment between the evolving requirements and the generated data models. As requirements change through iterations, this step number helps maintain traceability and version consistency across the database architecture development process.
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 Database agent generates a single database model for a target table during the database design process.
Each AI call produces exactly one target table model. If the agent determines that additional child tables are needed (e.g. for 1NF decomposition), it declares them as lightweight designs in AutoBeDatabaseSchemaDefinition.newDesigns so they can be generated by their own dedicated pipeline calls.
Each event represents the completion of one target table within a namespace. Multiple events are emitted per namespace, one per target table, enabling fine-grained progress tracking and parallel generation.
Author
Samchon