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dataset'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  inputSchema: jsonSchemaField.describe('JSON Schema for validating item input'),\n  groundTruthSchema: jsonSchemaField.describe('JSON Schema for validating item groundTruth'),\n  requestContextSchema: jsonSchemaField.describe('JSON Schema describing expected request context shape'),\n  targetType: z.string().optional().describe('Target entity type (e.g. agent, workflow, scorer)'),\n  targetIds: z.array(z.string()).optional().describe('IDs of target entities this dataset is attached to'),\n  scorerIds: z.array(z.string()).optional().describe('IDs of scorers attached to this dataset'),\n});\n\nexport const updateDatasetBodySchema = z.object({\n  name: z.string().optional().describe('Name of the dataset'),\n  description: z.string().optional().describe('Description of the dataset'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  inputSchema: jsonSchemaField.describe('JSON Schema for validating item input'),\n  groundTruthSchema: jsonSchemaField.describe('JSON Schema for validating item groundTruth'),\n  requestContextSchema: jsonSchemaField.describe('JSON Schema describing expected request context shape'),\n  tags: z.array(z.string()).optional().describe('Tag definitions for categorizing experiment results'),\n  targetType: z.string().optional().describe('Target entity type (e.g. agent, workflow, scorer)'),\n  targetIds: z.array(z.string()).optional().describe('IDs of target entities this dataset is attached to'),\n  scorerIds: z.array(z.string()).optional().nullable().describe('IDs of scorers attached to this dataset'),\n});\n\nexport const addItemBodySchema = z.object({\n  input: z.unknown().describe('Input data for the dataset item'),\n  groundTruth: z.unknown().optional().describe('Expected output for comparison'),\n  expectedTrajectory: trajectoryExpectationSchema,\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Request context preset for this item'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  source: datasetItemSourceSchema,\n});\n\nexport const updateItemBodySchema = z.object({\n  input: z.unknown().optional().describe('Input data for the dataset item'),\n  groundTruth: z.unknown().optional().describe('Expected output for comparison'),\n  expectedTrajectory: trajectoryExpectationSchema,\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Request context preset for this item'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  source: datasetItemSourceSchema,\n});\n\nexport const triggerExperimentBodySchema = z.object({\n  targetType: z.enum(['agent', 'workflow', 'scorer']).describe('Type of target to run against'),\n  targetId: z.string().describe('ID of the target'),\n  scorerIds: z.array(z.string()).optional().describe('IDs of scorers to apply'),\n  version: z.coerce.number().int().optional().describe('Pin to specific dataset version'),\n  agentVersion: z.string().optional().describe('Agent version ID to use for experiment'),\n  maxConcurrency: z.number().optional().describe('Maximum concurrent executions'),\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Global request context passed to the target'),\n  versions: z\n    .object({\n      agents: z\n        .record(\n          z.string(),\n          z.union([z.object({ versionId: z.string() }), z.object({ status: z.enum(['draft', 'published']) })]),\n        )\n        .optional(),\n      defaultStatus: z.enum(['draft', 'published']).optional(),\n    })\n    .optional()\n    .describe('Version overrides for sub-agent delegation during experiment execution'),\n});\n\nexport const compareExperimentsBodySchema = z.object({\n  experimentIdA: z.string().describe('ID of baseline experiment'),\n  experimentIdB: z.string().describe('ID of candidate experiment'),\n});\n\n// ============================================================================\n// Response Schemas\n// ============================================================================\n\n// Dataset entity schema\nexport const datasetResponseSchema = z.object({\n  id: z.string(),\n  name: z.string(),\n  description: z.string().optional().nullable(),\n  metadata: z.record(z.string(), z.unknown()).optional().nullable(),\n  inputSchema: z.record(z.string(), z.unknown()).optional(),\n  groundTruthSchema: z.record(z.string(), z.unknown()).optional(),\n  requestContextSchema: z.record(z.string(), z.unknown()).optional(),\n  tags: z.array(z.string()).optional().nullable(),\n  targetType: z.string().optional().nullable(),\n  targetIds: z.array(z.string()).optional().nullable(),\n  scorerIds: z.array(z.string()).optional().nullable(),\n  version: z.number().int(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Dataset item entity schema\nexport const datasetItemResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  datasetVersion: z.number().int(),\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n  expectedTrajectory: z.unknown().optional(),\n  requestContext: z.record(z.string(), z.unknown()).optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  source: datasetItemSourceSchema,\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Experiment entity schema\nexport const experimentResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string().nullable(),\n  datasetVersion: z.number().int().nullable(),\n  agentVersion: z.string().nullable().optional(),\n  targetType: z.enum(['agent', 'workflow', 'scorer', 'processor']),\n  targetId: z.string(),\n  name: z.string().optional(),\n  description: z.string().optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  status: z.enum(['pending', 'running', 'completed', 'failed']),\n  totalItems: z.number(),\n  succeededCount: z.number(),\n  failedCount: z.number(),\n  skippedCount: z.number(),\n  startedAt: z.coerce.date().nullable(),\n  completedAt: z.coerce.date().nullable(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Scorer result schema\nexport const scorerResultSchema = z.object({\n  scorerId: z.string(),\n  scorerName: z.string(),\n  score: z.number().nullable(),\n  reason: z.string().nullable(),\n  error: z.string().nullable(),\n});\n\n// Experiment result entity schema\nexport const experimentResultResponseSchema = z.object({\n  id: z.string(),\n  experimentId: z.string(),\n  itemId: z.string(),\n  itemDatasetVersion: z.number().int().nullable(),\n  input: z.unknown(),\n  output: z.unknown().nullable(),\n  groundTruth: z.unknown().nullable(),\n  expectedTrajectory: z.unknown().optional(),\n  error: z\n    .object({\n      message: z.string(),\n      stack: z.string().optional(),\n      code: z.string().optional(),\n    })\n    .nullable(),\n  startedAt: z.coerce.date(),\n  completedAt: z.coerce.date(),\n  retryCount: z.number(),\n  traceId: z.string().nullable(),\n  status: z.enum(['needs-review', 'reviewed', 'complete']).nullable().optional(),\n  tags: z.array(z.string()).nullable().optional(),\n  createdAt: z.coerce.date(),\n});\n\nexport const updateExperimentResultBodySchema = z.object({\n  status: z.enum(['needs-review', 'reviewed', 'complete']).nullable().optional(),\n  tags: z.array(z.string()).optional(),\n});\n\n// Comparison item schema (MVP shape)\nconst comparisonItemSchema = z.object({\n  itemId: z.string(),\n  input: z.unknown().nullable(),\n  groundTruth: z.unknown().nullable(),\n  results: z.record(\n    z.string(),\n    z\n      .object({\n        output: z.unknown().nullable(),\n        scores: z.record(z.string(), z.number().nullable()),\n      })\n      .nullable(),\n  ),\n});\n\n// Comparison result schema\nexport const comparisonResponseSchema = z.object({\n  baselineId: z.string(),\n  items: z.array(comparisonItemSchema),\n});\n\n// Experiment summary schema (returned by trigger experiment)\n// Note: completedAt is nullable for pending/running experiments (async trigger)\nexport const experimentSummaryResponseSchema = z.object({\n  experimentId: z.string(),\n  status: z.enum(['pending', 'running', 'completed', 'failed']),\n  totalItems: z.number(),\n  succeededCount: z.number(),\n  failedCount: z.number(),\n  startedAt: z.coerce.date(),\n  completedAt: z.coerce.date().nullable(),\n  results: z.array(\n    z.object({\n      itemId: z.string(),\n      itemDatasetVersion: z.number().int().nullable(),\n      input: z.unknown(),\n      output: z.unknown().nullable(),\n      groundTruth: z.unknown().nullable(),\n      error: z.string().nullable(),\n      startedAt: z.coerce.date(),\n      completedAt: z.coerce.date(),\n      retryCount: z.number(),\n      scores: z.array(\n        z.object({\n          scorerId: z.string(),\n          scorerName: z.string(),\n          score: z.number().nullable(),\n          reason: z.string().nullable(),\n          error: z.string().nullable(),\n        }),\n      ),\n    }),\n  ),\n});\n\n// ============================================================================\n// List Response Schemas\n// ============================================================================\n\nexport const listDatasetsResponseSchema = z.object({\n  datasets: z.array(datasetResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listItemsResponseSchema = z.object({\n  items: z.array(datasetItemResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listExperimentsResponseSchema = z.object({\n  experiments: z.array(experimentResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listExperimentResultsResponseSchema = z.object({\n  results: z.array(experimentResultResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const experimentReviewCountsSchema = z.object({\n  experimentId: z.string(),\n  total: z.number().int(),\n  needsReview: z.number().int(),\n  reviewed: z.number().int(),\n  complete: z.number().int(),\n});\n\nexport const reviewSummaryResponseSchema = z.object({\n  counts: z.array(experimentReviewCountsSchema),\n});\n\n// ============================================================================\n// Version Schemas\n// ============================================================================\n\n// Path params for item version routes\nexport const datasetItemVersionPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  itemId: z.string().describe('Unique identifier for the dataset item'),\n  datasetVersion: z.coerce.number().int().describe('Dataset version number'),\n});\n\n// Item history row response schema (SCD-2 DatasetItemRow shape)\nexport const itemVersionResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  datasetVersion: z.number().int(),\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n  expectedTrajectory: z.unknown().optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  validTo: z.number().int().nullable(),\n  isDeleted: z.boolean(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\nexport const listItemVersionsResponseSchema = z.object({\n  history: z.array(itemVersionResponseSchema),\n});\n\n// Dataset version response schema\nexport const datasetVersionResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  version: z.number().int(),\n  createdAt: z.coerce.date(),\n});\n\nexport const listDatasetVersionsResponseSchema = z.object({\n  versions: z.array(datasetVersionResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\n// ============================================================================\n// Batch Operation Schemas\n// ============================================================================\n\nexport const batchInsertItemsBodySchema = z.object({\n  items: z.array(\n    z.object({\n      input: z.unknown(),\n      groundTruth: z.unknown().optional(),\n      expectedTrajectory: trajectoryExpectationSchema,\n      requestContext: z.record(z.string(), z.unknown()).optional(),\n      metadata: z.record(z.string(), z.unknown()).optional(),\n      source: datasetItemSourceSchema,\n    }),\n  ),\n});\n\nexport const batchInsertItemsResponseSchema = z.object({\n  items: z.array(datasetItemResponseSchema),\n  count: z.number(),\n});\n\nexport const batchDeleteItemsBodySchema = z.object({\n  itemIds: z.array(z.string()),\n});\n\nexport const batchDeleteItemsResponseSchema = z.object({\n  success: z.boolean(),\n  deletedCount: z.number(),\n});\n\n// ============================================================================\n// AI Generation Schemas\n// ============================================================================\n\nexport const generateItemsBodySchema = z.object({\n  modelId: z.string().describe('Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")'),\n  prompt: z.string().describe('Description of the kind of test data to generate'),\n  count: z.number().int().min(1).max(50).default(5).describe('Number of items to generate'),\n  agentContext: z\n    .object({\n      description: z.string().optional(),\n      instructions: z.string().optional(),\n      tools: z.array(z.string()).optional(),\n    })\n    .optional()\n    .describe('Context about the agent to generate relevant test data'),\n});\n\nconst generatedItemSchema = z.object({\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n});\n\nexport const generateItemsResponseSchema = z.object({\n  items: z.array(generatedItemSchema),\n});\n\n// ============================================================================\n// Cluster Failures\n// ============================================================================\n\nexport const clusterFailuresBodySchema = z.object({\n  modelId: z.string().describe('Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")'),\n  items: z\n    .array(\n      z.object({\n        id: z.string(),\n        input: z.unknown(),\n        output: z.unknown().optional(),\n        error: z.string().optional(),\n        scores: z.record(z.string(), z.number()).optional(),\n        existingTags: z.array(z.string()).optional().describe('Tags already applied to this item'),\n      }),\n    )\n    .min(1)\n    .max(200)\n    .describe('Failure items to cluster'),\n  availableTags: z\n    .array(z.string())\n    .optional()\n    .describe('Existing tag vocabulary from the dataset. The LLM should prefer reusing these tags when applicable.'),\n  prompt: z\n    .string()\n    .optional()\n    .describe('Optional user instructions to guide the analysis (e.g., \"focus on tool usage failures\")'),\n});\n\nconst failureClusterSchema = z.object({\n  id: z.string().describe('A unique cluster identifier'),\n  label: z.string().describe('Short label for this failure pattern'),\n  description: z.string().describe('Description of the common pattern'),\n  itemIds: z.array(z.string()).describe('IDs of items belonging to this cluster'),\n});\n\nexport const clusterFailuresResponseSchema = z.object({\n  clusters: z.array(failureClusterSchema),\n  /** Per-item proposed tag assignments. Each entry maps an item ID to the tags the LLM suggests adding. */\n  proposedTags: z\n    .array(\n      z.object({\n        itemId: z.string(),\n        tags: z.array(z.string()),\n        reason: z.string().describe('Brief explanation of why these tags were assigned to this item'),\n      }),\n    )\n    .optional(),\n});\n"]}