---
title: "Update task"
method: PATCH
path: "/v2/tasks/{task_id}"
tags: ["Tasks"]
---

# Update task

`PATCH /v2/tasks/{task_id}`

Update a task's mutable fields. At least one field must be provided.
Omitted fields are left unchanged.

**Payload Requirements**
- At least one mutable field must be provided.
- When `evaluators` is provided, the entire evaluator list is replaced.
- `sampling_rate` and `is_continuous` are only applicable for project-based tasks.
- Fields not valid for the task's type return 400 (e.g. `run_configuration` on an evaluation task).
- System-managed fields (`id`, `type`, `created_at`, `updated_at`) cannot be modified.
- `evaluator_version_id` pins an evaluator to one version. Because `evaluators`
  replaces the whole list, each entry states its own pin: send a version ID to pin,
  and omit the field or send null to run the latest version. Omit `evaluators`
  entirely to leave the existing attachments — and their pins — untouched.
- A pinned version must belong to the evaluator named by `evaluator_id`, and every
  evaluator on the task must resolve to the same data scope. Both return 422.

**Valid example** (update evaluation task)
```json
{
  "name": "Updated Hallucination Check",
  "sampling_rate": 0.5,
  "query_filter": "metadata.environment = 'staging'"
}
```

**Valid example** (pin one evaluator, leave the other on latest)
```json
{
  "evaluators": [
    {
      "evaluator_id": "RXZhbHVhdG9yOjEyOmFCY0Q=",
      "evaluator_version_id": "RXZhbHVhdG9yVmVyc2lvbjo5OTphQmNE"
    },
    {
      "evaluator_id": "RXZhbHVhdG9yOjEzOmFCY0Q="
    }
  ]
}
```

**Invalid example** (no fields provided)
```json
{}
```

<Note>This endpoint is in beta, read more [here](https://arize.com/docs/ax/rest-reference#api-version-stages).</Note>

## Path parameters

- `task_id` string, required — A universally unique identifier (base64-encoded opaque string).

## Request body

- union — PATCH body for `PATCH /v2/tasks/{task_id}`. The server derives the task type from the URL's task record and selects the appropriate schema; the body itself does not carry a `type` field. | Task type | Schema | |---|---| | `TEMPLATE_EVALUATION` | `UpdateEvaluationTaskRequest` | | `CODE_EVALUATION` | `UpdateEvaluationTaskRequest` | | `RUN_EXPERIMENT` | `UpdateRunExperimentTaskRequest` | Sending a field that is not valid for the resolved task type returns 400 (e.g. `evaluators` on a `RUN_EXPERIMENT` task, or `run_configuration` on an evaluation task).
  - UpdateEvaluationTaskRequest — PATCH body for `TEMPLATE_EVALUATION` and `CODE_EVALUATION` tasks. The two types share the same updatable shape; the server derives the task type from the URL's task record. At least one field must be provided. Evaluators carry one of two mutually exclusive shapes depending on their data granularity. Span evaluators use task-level `query_filter` plus per-evaluator `column_mappings`/`query_filter`. Trace/session evaluators use task-level `query_filters` plus per-evaluator `query_mappings`. Mixing the two shapes returns 400.
    - `name` string — New task name.
    - `sampling_rate` number — Sampling rate between 0 and 1. Only applicable for project-based tasks.
    - `is_continuous` boolean — Whether the task runs continuously. Only applicable for project-based tasks.
    - `query_filter` string, nullable — Task-level query filter (span shape). Pass `null` to clear. Mutually exclusive with `query_filters`.
    - `query_filters` TaskQueryFiltersInput — Combined named-query filters and boolean expression for create/update requests (trace/session shape). Supply this object OR `query_filter` (span shape) — not both.
      - `filters` TaskQueryFilterInput[], required — Named query filters (1-5 entries) with unique `A`-`E` ids. Each entry pairs a single-letter id with a filter expression.
        - `id` string, required — Single-letter query id, one of `A`-`E`. Unique within the task. Referenced by `query_filters.expression` and by each evaluator's `query_mappings`.
        - `filter` string, required — The query filter expression for this named query.
      - `expression` string — Boolean expression combining the `filters` ids (e.g. `A AND B`). Optional when exactly one filter is declared; required when two or more are declared.
    - `evaluators` TaskEvaluatorInput[] — Replaces the entire evaluator list. At least one evaluator is required when provided. Omit the field to leave evaluators unchanged.
      - union — An evaluator attachment supplied when creating or updating a task. At least one entry is required on evaluation-task requests. Evaluators carry one of two mutually exclusive shapes: span evaluators use `query_filter` + `column_mappings`; trace/session evaluators use `query_mappings`.
        - SpanEvaluatorInput — Span-granularity evaluator input. Uses `query_filter` and `column_mappings`.
          - `evaluator_id` string, required — Evaluator identifier (base64). Duplicates are not allowed.
          - `evaluator_version_id` string, nullable — Pin this evaluator to a specific version (base64). Defaults to null, which always runs the evaluator's latest version; omitting the field and sending null are equivalent. Must be a version of the evaluator named by `evaluator_id`, otherwise the request returns 422.
          - `query_filter` string — Per-evaluator query filter (span shape). Combined with the task-level filter (AND).
          - `column_mappings` object — Maps evaluator template variable names to data source column names (span shape).
        - TraceOrSessionEvaluatorInput — Trace/session-granularity evaluator input. Uses `query_mappings`.
          - `evaluator_id` string, required — Evaluator identifier (base64). Duplicates are not allowed.
          - `evaluator_version_id` string, nullable — Pin this evaluator to a specific version (base64). Defaults to null, which always runs the evaluator's latest version; omitting the field and sending null are equivalent. Must be a version of the evaluator named by `evaluator_id`, otherwise the request returns 422.
          - `query_mappings` TaskQueryMappingInput[], required — Per-evaluator variable-to-query mappings (trace/session shape).
            - `variable_name` string, required — The evaluator template variable this mapping populates.
            - `query_ids` string[], required — Declared query ids (`A`-`E`) whose matching units feed this variable. An empty list means "any declared query" (valid for session-level variables that match all spans in the conversation). Every id must be declared in the task's `query_filters.filters`.
            - `attribute_path` string, required — Span attribute path (e.g. `attributes.input.value`) resolved within each admitted unit to populate `variable_name`.
  - UpdateRunExperimentTaskRequest — PATCH body for `RUN_EXPERIMENT` tasks. The server derives the task type from the URL's task record. At least one of `name` or `run_configuration` must be provided. When `run_configuration` is provided the stored config is fully replaced (existing configs are marked inactive and the new config is inserted atomically in a transaction).
    - `name` string — New task name.
    - `run_configuration` union — Strict request form of an experiment execution configuration. Exactly one variant must be supplied, identified by `experiment_type`.
      - LlmGenerationRunConfigRequest — Strict request configuration for running an LLM prompt against each dataset example.
        - `experiment_type` 'LLM_GENERATION', required — Discriminator. Must be `"LLM_GENERATION"`.
        - `ai_integration_id` string, required — AI integration identifier (base64).
        - `model_name` string — Model name (e.g. `gpt-4o`). Falls back to the integration's default if omitted.
        - `messages` LLMMessageRequest[], required — Array of message objects (at least one).
          - `role` 'USER' | 'ASSISTANT' | 'SYSTEM' | 'TOOL', required — The role of the message author
          - `content` string, nullable — The content of the message
          - `tool_call_id` string — The ID of the tool call this message is responding to
          - `tool_calls` ToolCallRequest[] — Tool calls generated by the model
            - `id` string — The ID of the tool call
            - `type` 'FUNCTION', required — The type of tool call
            - `function` ToolCallFunctionRequest, required — The function to call (strict request form of ToolCallFunction)
              - …
        - `input_variable_format` 'F_STRING' | 'MUSTACHE' | 'NONE', required — The format for input variables in the prompt messages. Defaults to `F_STRING` if not provided. - `F_STRING`: Single curly braces ({variable_name}) - `MUSTACHE`: Double curly braces ({{variable_name}}) - `NONE`: **Deprecated.** Treated as `F_STRING`. Will be removed in a future version.
        - `invocation_parameters` InvocationParamsRequest — Parameters for the LLM invocation in a write request (strict form of InvocationParams; leaf schemas use *Request variants)
          - `temperature` number — Sampling temperature (higher = more random)
          - `max_tokens` integer — Maximum number of tokens to generate
          - `max_completion_tokens` integer — Maximum number of completion tokens to generate
          - `top_p` number — Nucleus sampling parameter
          - `frequency_penalty` number — Frequency penalty (-2.0 to 2.0)
          - `presence_penalty` number — Presence penalty (-2.0 to 2.0)
          - `stop` string[] — Stop sequences
          - `response_format` ResponseFormatRequest — Response format configuration in a write request (strict form of ResponseFormat)
            - `type` 'TEXT' | 'JSON_OBJECT' | 'JSON_SCHEMA' — The response format type
            - `json_schema` JsonSchemaConfigRequest — JSON schema configuration in a write request (strict form of JsonSchemaConfig)
              - …
          - `tool_config` ToolConfigRequest — Tool configuration in a write request (strict form of ToolConfig)
            - `tools` ToolDefinition[] — List of tool definitions available to the model
              - …
            - `tool_choice` unknown
          - `top_k` integer — Top-K sampling parameter. A top-K of 1 means the next selected token is the most probable (greedy decoding).
          - `thinking_level` string — Controls how much reasoning the model performs before responding. Supported by Gemini 3.x models. Accepted values: 'low', 'high'.
          - `thinking_budget` integer — Maximum tokens the model may use for internal reasoning. Supported by Gemini 2.5 models. Range: 0-24576 (Flash/Flash-Lite) or 128-32768 (Pro). Set 0 to disable thinking on Flash models.
          - `reasoning_effort` string — Controls how much reasoning the model performs before responding. Supported by OpenAI o-series and GPT-5 models. o-series: 'low' | 'medium' | 'high'. GPT-5: 'none' | 'low' | 'medium' | 'high' | 'xhigh'.
          - `verbosity` string — Controls the verbosity of model output. Supported by OpenAI GPT-5 series. Accepted values: 'low' | 'medium' | 'high'.
        - `provider_parameters` object — Provider-specific parameters. Defaults to `{}` (no overrides) if omitted.
        - `tool_config` ToolConfigRequest — Tool configuration in a write request (strict form of ToolConfig)
          - `tools` ToolDefinition[] — List of tool definitions available to the model
          - `tool_choice` unknown
        - `prompt_version_id` string, nullable — Prompt version identifier (base64). Links to a Prompt Hub version for traceability.
      - TemplateEvaluationRunConfigRequest — Strict request configuration for running a template-based LLM evaluator.
        - `experiment_type` 'TEMPLATE_EVALUATION', required — Discriminator. Must be `"TEMPLATE_EVALUATION"`.
        - `ai_integration_id` string, required — AI integration identifier (base64). The LLM that judges each example.
        - `model_name` string — Model name (e.g. `gpt-4o`). Falls back to the integration's default if omitted.
        - `template` string, required — The evaluation prompt template. Use `{{variable}}` placeholders that map to dataset column paths via `column_mapping`.
        - `provide_explanation` boolean, required — Whether to ask the LLM to include a written explanation alongside the score/label.
        - `classification_choices` object — Map of choice label to numeric score (e.g. `{"relevant": 1, "irrelevant": 0}`).
        - `column_mapping` object — Maps template variable names to dataset column paths.
        - `evaluator_version_id` string, nullable — EvaluatorVersion identifier (base64). Links this run to an Eval Hub evaluator version.
        - `invocation_parameters` InvocationParamsRequest — Parameters for the LLM invocation in a write request (strict form of InvocationParams; leaf schemas use *Request variants)
          - `temperature` number — Sampling temperature (higher = more random)
          - `max_tokens` integer — Maximum number of tokens to generate
          - `max_completion_tokens` integer — Maximum number of completion tokens to generate
          - `top_p` number — Nucleus sampling parameter
          - `frequency_penalty` number — Frequency penalty (-2.0 to 2.0)
          - `presence_penalty` number — Presence penalty (-2.0 to 2.0)
          - `stop` string[] — Stop sequences
          - `response_format` ResponseFormatRequest — Response format configuration in a write request (strict form of ResponseFormat)
            - `type` 'TEXT' | 'JSON_OBJECT' | 'JSON_SCHEMA' — The response format type
            - `json_schema` JsonSchemaConfigRequest — JSON schema configuration in a write request (strict form of JsonSchemaConfig)
              - …
          - `tool_config` ToolConfigRequest — Tool configuration in a write request (strict form of ToolConfig)
            - `tools` ToolDefinition[] — List of tool definitions available to the model
              - …
            - `tool_choice` unknown
          - `top_k` integer — Top-K sampling parameter. A top-K of 1 means the next selected token is the most probable (greedy decoding).
          - `thinking_level` string — Controls how much reasoning the model performs before responding. Supported by Gemini 3.x models. Accepted values: 'low', 'high'.
          - `thinking_budget` integer — Maximum tokens the model may use for internal reasoning. Supported by Gemini 2.5 models. Range: 0-24576 (Flash/Flash-Lite) or 128-32768 (Pro). Set 0 to disable thinking on Flash models.
          - `reasoning_effort` string — Controls how much reasoning the model performs before responding. Supported by OpenAI o-series and GPT-5 models. o-series: 'low' | 'medium' | 'high'. GPT-5: 'none' | 'low' | 'medium' | 'high' | 'xhigh'.
          - `verbosity` string — Controls the verbosity of model output. Supported by OpenAI GPT-5 series. Accepted values: 'low' | 'medium' | 'high'.
        - `provider_parameters` object — Provider-specific parameters. Defaults to `{}` (no overrides) if omitted.
      - AgentCallRunConfigRequest — Strict request configuration for running an agent integration.
        - `experiment_type` 'AGENT_CALL', required — Discriminator. Must be `"AGENT_CALL"`.
        - `integration_id` string, required — Agent integration identifier (base64). The agent invoked for each dataset example. Must reference an integration of `type` `AGENT`; other integration types are rejected.
        - `input_template` object, required — JSON request body sent to the agent for each dataset example. Must be a JSON object whose values conform to the agent integration's input schema. Mustache placeholders (`{{column}}`) are substituted with each dataset row's values before the request is sent.

## Response `200`

Returns a single task object

- Task — A task is a typed, configurable unit of work that ties one or more evaluators to a data source (project or dataset). `RUN_EXPERIMENT` tasks additionally carry a `run_configuration` that defines the LLM, evaluator, or agent settings for each triggered run. Evaluation tasks (`TEMPLATE_EVALUATION` and `CODE_EVALUATION`) use one of two mutually exclusive query-filter shapes depending on the granularity of the data each evaluator processes: - **Span shape** — `query_filter` (task-level) plus per-evaluator `column_mappings`/`query_filter`. For tasks where each evaluated unit is a single span. `query_filters` is null. - **Trace/session shape** — `query_filters` (named `filters` plus optional `expression`) at the task level, and per-evaluator `query_mappings`. For tasks where each evaluated unit is a complete trace or session. `query_filter` is null. All evaluators on a task must use the same shape; mixing shapes returns 400.
  - `id` string, required — The unique identifier for the task
  - `name` string, required — The name of the task
  - `type` 'TEMPLATE_EVALUATION' | 'CODE_EVALUATION' | 'RUN_EXPERIMENT', required — The task type. - TEMPLATE_EVALUATION - An LLM template-based evaluation task. - CODE_EVALUATION - A code-based evaluation task. - RUN_EXPERIMENT - A task that runs experiments.
  - `project_id` string, nullable — The project identifier (base64). Present for project-based tasks.
  - `dataset_id` string, nullable — The dataset identifier (base64). Present for dataset-based tasks.
  - `sampling_rate` number, nullable — Sampling rate between 0 and 1. Only applicable for project-based tasks.
  - `is_continuous` boolean, required — Whether the task runs continuously on incoming data.
  - `query_filter` string, nullable, required — Task-level query filter applied to all data. Span-granularity shape only. Null when the task uses the trace/session shape (`query_filters`). Mutually exclusive with `query_filters`.
  - `query_filters` TaskQueryFilters — Combined named-query filters and boolean expression for the trace/session shape. Supply this object OR `query_filter` (span shape) — not both.
    - `filters` TaskQueryFilter[], required — Named query filters (1-5 entries) with unique `A`-`E` ids. Each entry pairs a single-letter id with a filter expression.
      - `id` string, required — Single-letter query id, one of `A`-`E`. Unique within the task. Referenced by `query_filters.expression` and by each evaluator's `query_mappings`.
      - `filter` string, required — The query filter expression for this named query.
    - `expression` string — Boolean expression combining the `filters` ids (e.g. `A AND B`). Optional when exactly one filter is declared; required when two or more are declared. When a client omits `expression` on a single-filter create, GET echoes a synthesized expression equal to the lone query id (e.g. `A`).
  - `evaluators` TaskEvaluator[], required — The evaluators attached to this task. Empty for run_experiment tasks.
    - `evaluator_id` string, required — Evaluator identifier (base64).
    - `evaluator_name` string, required — The name of the attached evaluator.
    - `evaluator_version_id` string, nullable, required — The evaluator version this attachment is pinned to (base64). Null is the default and means the attachment is not pinned, so it runs the evaluator's latest version.
    - `query_filter` string, nullable, required — Per-evaluator query filter, combined with the task-level filter (AND). Span-granularity shape only; null for trace/session evaluators.
    - `column_mappings` object, nullable, required — Maps evaluator template variable names to data source column names. Span-granularity shape only; null for trace/session evaluators (which use `query_mappings`).
    - `query_mappings` TaskQueryMapping[], nullable — Maps each evaluator variable to one or more declared query ids plus an attribute path, for trace/session evaluators. Present only on trace/session tasks; null on span tasks (which use `column_mappings`).
      - `variable_name` string, required — The evaluator template variable this mapping populates.
      - `query_ids` string[], required — Declared query ids (`A`-`E`) whose matching units feed this variable. An empty list means "any declared query" (valid for session-level variables that match all spans in the conversation). Every id must be declared in the task's `query_filters.filters`.
      - `attribute_path` string, required — Span attribute path (e.g. `attributes.input.value`) resolved within each admitted unit to populate `variable_name`.
  - `experiment_ids` string[], required — Experiment identifiers (base64) for dataset-based tasks.
  - `run_configuration` union — Experiment execution configuration for a `RUN_EXPERIMENT` task. Exactly one variant must be supplied, identified by `experiment_type`. All fields sit at the top level alongside `experiment_type` (flat — no wrapper sub-object).
    - LlmGenerationRunConfig — Configuration for running an LLM prompt against each dataset example.
      - `experiment_type` 'LLM_GENERATION', required — Discriminator. Must be `"LLM_GENERATION"`.
      - `ai_integration_id` string, required — AI integration identifier (base64).
      - `model_name` string — Model name (e.g. `gpt-4o`). Falls back to the integration's default if omitted.
      - `messages` LLMMessage[], required — Array of message objects (at least one).
        - `role` 'USER' | 'ASSISTANT' | 'SYSTEM' | 'TOOL', required — The role of the message author
        - `content` string, nullable — The content of the message
        - `tool_call_id` string — The ID of the tool call this message is responding to
        - `tool_calls` ToolCall[] — Tool calls generated by the model
          - `id` string — The ID of the tool call
          - `type` 'FUNCTION', required — The type of tool call
          - `function` ToolCallFunction, required — The function to call
            - `name` string, required — The name of the function
            - `arguments` string, required — The arguments to the function as a JSON string
      - `input_variable_format` 'F_STRING' | 'MUSTACHE' | 'NONE', required — The format for input variables in the prompt messages. Defaults to `F_STRING` if not provided. - `F_STRING`: Single curly braces ({variable_name}) - `MUSTACHE`: Double curly braces ({{variable_name}}) - `NONE`: **Deprecated.** Treated as `F_STRING`. Will be removed in a future version.
      - `invocation_parameters` InvocationParams — Parameters for the LLM invocation
        - `temperature` number — Sampling temperature (higher = more random)
        - `max_tokens` integer — Maximum number of tokens to generate
        - `max_completion_tokens` integer — Maximum number of completion tokens to generate
        - `top_p` number — Nucleus sampling parameter
        - `frequency_penalty` number — Frequency penalty (-2.0 to 2.0)
        - `presence_penalty` number — Presence penalty (-2.0 to 2.0)
        - `stop` string[] — Stop sequences
        - `response_format` ResponseFormat — Response format configuration
          - `type` 'TEXT' | 'JSON_OBJECT' | 'JSON_SCHEMA' — The response format type
          - `json_schema` JsonSchemaConfig — JSON schema configuration (when type is JSON_SCHEMA)
            - `name` string — The name of the JSON schema
            - `description` string — A description of the JSON schema
            - `schema` object — The JSON schema object
            - `strict` boolean — Whether to enforce strict schema validation. Defaults to `false`.
        - `tool_config` ToolConfig — Tool configuration for the LLM invocation
          - `tools` ToolDefinition[] — List of tool definitions available to the model
          - `tool_choice` unknown
        - `top_k` integer — Top-K sampling parameter. A top-K of 1 means the next selected token is the most probable (greedy decoding).
        - `thinking_level` string — Controls how much reasoning the model performs before responding. Supported by Gemini 3.x models. Accepted values: 'low', 'high'.
        - `thinking_budget` integer — Maximum tokens the model may use for internal reasoning. Supported by Gemini 2.5 models. Range: 0-24576 (Flash/Flash-Lite) or 128-32768 (Pro). Set 0 to disable thinking on Flash models.
        - `reasoning_effort` string — Controls how much reasoning the model performs before responding. Supported by OpenAI o-series and GPT-5 models. o-series: 'low' | 'medium' | 'high'. GPT-5: 'none' | 'low' | 'medium' | 'high' | 'xhigh'.
        - `verbosity` string — Controls the verbosity of model output. Supported by OpenAI GPT-5 series. Accepted values: 'low' | 'medium' | 'high'.
        - `service_tier` string — Processing tier for the request. Supported by OpenAI only, and only for models eligible for Priority processing. Accepted value: 'priority'. Omit to use the project default.
      - `provider_parameters` object — Provider-specific parameters. Defaults to `{}` (no overrides) if omitted.
      - `tool_config` ToolConfig — Tool configuration for the LLM invocation
        - `tools` ToolDefinition[] — List of tool definitions available to the model
        - `tool_choice` unknown
      - `prompt_version_id` string, nullable — Prompt version identifier (base64). Links to a Prompt Hub version for traceability.
    - TemplateEvaluationRunConfig — Configuration for running a template-based LLM evaluator against each dataset example.
      - `experiment_type` 'TEMPLATE_EVALUATION', required — Discriminator. Must be `"TEMPLATE_EVALUATION"`.
      - `ai_integration_id` string, required — AI integration identifier (base64). The LLM that judges each example.
      - `model_name` string — Model name (e.g. `gpt-4o`). Falls back to the integration's default if omitted.
      - `template` string, required — The evaluation prompt template. Use `{{variable}}` placeholders that map to dataset column paths via `column_mapping`.
      - `provide_explanation` boolean, required — Whether to ask the LLM to include a written explanation alongside the score/label.
      - `classification_choices` object — Map of choice label to numeric score (e.g. `{"relevant": 1, "irrelevant": 0}`).
      - `column_mapping` object — Maps template variable names to dataset column paths.
      - `evaluator_version_id` string, nullable — EvaluatorVersion identifier (base64). Links this run to an Eval Hub evaluator version.
      - `invocation_parameters` InvocationParams — Parameters for the LLM invocation
        - `temperature` number — Sampling temperature (higher = more random)
        - `max_tokens` integer — Maximum number of tokens to generate
        - `max_completion_tokens` integer — Maximum number of completion tokens to generate
        - `top_p` number — Nucleus sampling parameter
        - `frequency_penalty` number — Frequency penalty (-2.0 to 2.0)
        - `presence_penalty` number — Presence penalty (-2.0 to 2.0)
        - `stop` string[] — Stop sequences
        - `response_format` ResponseFormat — Response format configuration
          - `type` 'TEXT' | 'JSON_OBJECT' | 'JSON_SCHEMA' — The response format type
          - `json_schema` JsonSchemaConfig — JSON schema configuration (when type is JSON_SCHEMA)
            - `name` string — The name of the JSON schema
            - `description` string — A description of the JSON schema
            - `schema` object — The JSON schema object
            - `strict` boolean — Whether to enforce strict schema validation. Defaults to `false`.
        - `tool_config` ToolConfig — Tool configuration for the LLM invocation
          - `tools` ToolDefinition[] — List of tool definitions available to the model
          - `tool_choice` unknown
        - `top_k` integer — Top-K sampling parameter. A top-K of 1 means the next selected token is the most probable (greedy decoding).
        - `thinking_level` string — Controls how much reasoning the model performs before responding. Supported by Gemini 3.x models. Accepted values: 'low', 'high'.
        - `thinking_budget` integer — Maximum tokens the model may use for internal reasoning. Supported by Gemini 2.5 models. Range: 0-24576 (Flash/Flash-Lite) or 128-32768 (Pro). Set 0 to disable thinking on Flash models.
        - `reasoning_effort` string — Controls how much reasoning the model performs before responding. Supported by OpenAI o-series and GPT-5 models. o-series: 'low' | 'medium' | 'high'. GPT-5: 'none' | 'low' | 'medium' | 'high' | 'xhigh'.
        - `verbosity` string — Controls the verbosity of model output. Supported by OpenAI GPT-5 series. Accepted values: 'low' | 'medium' | 'high'.
        - `service_tier` string — Processing tier for the request. Supported by OpenAI only, and only for models eligible for Priority processing. Accepted value: 'priority'. Omit to use the project default.
      - `provider_parameters` object — Provider-specific parameters. Defaults to `{}` (no overrides) if omitted.
    - AgentCallRunConfig — Configuration for running an agent integration against each dataset example. The `input_template` is sent to the agent after Mustache substitution.
      - `experiment_type` 'AGENT_CALL', required — Discriminator. Must be `"AGENT_CALL"`.
      - `integration_id` string, required — Agent integration identifier (base64). The agent invoked for each dataset example. Must reference an integration of `type` `AGENT`; other integration types are rejected.
      - `input_template` object, required — JSON request body sent to the agent for each dataset example. Must be a JSON object whose values conform to the agent integration's input schema. Mustache placeholders (`{{column}}`) are substituted with each dataset row's values before the request is sent. The `dataset.` prefix is optional — `{{column}}` and `{{dataset.column}}` are equivalent, and responses (create, update, and read) always echo the normalized `{{column}}` form.
  - `last_run_at` string, date-time, nullable, required — When the task was last run.
  - `created_at` string, date-time, required — When the task was created.
  - `updated_at` string, date-time, required — When the task was last updated.
  - `created_by_user_id` string, nullable, required — The unique identifier for the user who created the task.

## Other responses

- `400` — Invalid request
- `401` — Authentication is required
- `403` — Insufficient permissions to access this resource
- `404` — Not found
- `422` — Unprocessable entity
- `429` — Rate limit exceeded

## Changes

> 16 revisions in range; 1 not diffed.

- **2026-08-28** `f63ac334fd4a` — 1 breaking, 4 warning, 4 info
  - the `anyOf[#/components/schemas/UpdateEvaluationTaskRequest]/evaluators/items/` request property type/format changed from `object`/`` to ``/``
  - removed the request property `anyOf[#/components/schemas/UpdateEvaluationTaskRequest]/evaluators/items/column_mappings`
  - removed the request property `anyOf[#/components/schemas/UpdateEvaluationTaskRequest]/evaluators/items/evaluator_id`
  - removed the request property `anyOf[#/components/schemas/UpdateEvaluationTaskRequest]/evaluators/items/evaluator_version_id`
  - …5 more
- **2026-08-27** `9263b98aaefb` — 2 info
  - added the optional property `run_configuration/allOf[#/components/schemas/RunConfiguration]/oneOf[#/components/schemas/LlmGenerationRunConfig]/invocation_parameters/service_tier` to the response with the `200` status
  - added the optional property `run_configuration/allOf[#/components/schemas/RunConfiguration]/oneOf[#/components/schemas/TemplateEvaluationRunConfig]/invocation_parameters/service_tier` to the response with the `200` status
- …earlier changes not shown

[Full history](https://skmtc.dev/arize-ai/apis/arize-rest-api/changes/v2/tasks/:task_id/patch.md)

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[API](https://skmtc.dev/arize-ai/apis/arize-rest-api.md) · [All operations](https://skmtc.dev/arize-ai/apis/arize-rest-api/llms.txt) · [OpenAPI document](https://skmtc-service-production.skmtc.workers.dev/v1/apis/arize-ai/arize-rest-api/revisions/6521535a37bb/schema)