Retrieves an optimization job by its identifier.

The ID of the job.

The API version to use for this operation.

Recommended number of seconds to wait before polling again.

Server-assigned unique identifier.

Partial or terminal result produced by an agent optimization job.

Summary of candidates produced by an agent optimization job.

Number of completed candidates. Does not include the baseline.

Candidate ID of the original baseline. Omitted until the baseline candidate is available.

Candidate ID of the highest-scoring candidate for evaluated jobs, or the selected transformed output for non-comparative jobs. Omitted until a candidate has been selected.

Normalized score of the baseline candidate from 0.0 to 1.0 for a comparatively evaluated job. Omitted otherwise and until baseline evaluation completes.

Best normalized score observed from 0.0 to 1.0 for a comparatively evaluated job. Omitted otherwise and until a candidate completes evaluation.

Promotion metadata recorded for a candidate.

The name of the agent.

The version identifier of the agent.

Aggregate token usage per stage and model. Always present; empty array when no calls were measured.

Aggregate latency per stage and model. Always present; empty when no server-measured latency is available.

Reason the optimization process ended. Early termination reasons still indicate a successful job; completed candidates remain available.

Extensible status values shared by Foundry jobs.

The public explanation for this block.

The public continuation instruction.

Human-readable label. Omitted when no label is needed; it has no uniqueness, routing, or idempotency semantics.

Base target configuration for an optimization job.

Identifies the Foundry agent that owns the configuration being optimized.

Foundry-agent target discriminator.

Registered Foundry agent name.

Pinned agent version. Omitted to resolve and pin the latest version.

Identifies a model used by an optimization or evaluation step.

A non-empty Foundry model name or existing deployment name.

Base model for type-specific optimization configuration.

Configuration for optimizing an agent for measured quality.

Optimization type discriminator.

Goal for an agent-optimization job.

Reusable quality-measurement configuration.

Base discriminated model for an optimization evaluation set.

Evaluation set containing independent single-turn inputs.

Target-completion set discriminator.

Base source for a target-completion optimization evaluation set.

Supplies single-turn rows directly in the optimization request.

Inline source discriminator.

Target-completion test cases.

References an explicitly versioned registered Foundry dataset for target-completion evaluation.

Registered dataset source discriminator.

Registered dataset name.

Registered dataset version.

Evaluation set containing scenarios for simulated conversations.

User-conversation-simulation set discriminator.

Base source for a user-conversation-simulation optimization evaluation set.

Supplies Evals user-conversation simulation test cases directly in the optimization request.

Inline source discriminator.

Conversation test scenarios.

References an explicitly versioned registered Foundry dataset for user-conversation-simulation evaluation.

Registered dataset source discriminator.

Registered dataset name.

Registered dataset version.

Configures the number, length, audio effects, and simulated user behavior of conversations.

Hard limit on turns in each conversation. When omitted, the service defaults to 20.

Number of independent conversation repetitions for each test case. Defaults to 1 when not specified at either the data-source or test-case level.

Target number of turns in each conversation. The effective value cannot exceed the effective max_num_turns. When omitted, no target is set and the simulation model determines the conversation length dynamically from the scenario.

Configures effects applied to simulated conversation audio.

Effects to apply to simulated audio.

Volume of the configured audio effects, as a percentage from 1 through 100. When omitted, the service defaults to 15.

Configures conversation behavior for the simulated user.

Configuration for simulated user interruption behavior.

Configures the default interruption behavior for the simulated user.

The interruption type, always default.

Base discriminated model for an optimization evaluation set.

Evaluation set containing independent single-turn inputs.

Target-completion set discriminator.

Base source for a target-completion optimization evaluation set.

Supplies single-turn rows directly in the optimization request.

Inline source discriminator.

Target-completion test cases.

References an explicitly versioned registered Foundry dataset for target-completion evaluation.

Registered dataset source discriminator.

Registered dataset name.

Registered dataset version.

Evaluation set containing scenarios for simulated conversations.

User-conversation-simulation set discriminator.

Base source for a user-conversation-simulation optimization evaluation set.

Supplies Evals user-conversation simulation test cases directly in the optimization request.

Inline source discriminator.

Conversation test scenarios.

References an explicitly versioned registered Foundry dataset for user-conversation-simulation evaluation.

Registered dataset source discriminator.

Registered dataset name.

Registered dataset version.

Configures the number, length, audio effects, and simulated user behavior of conversations.

Hard limit on turns in each conversation. When omitted, the service defaults to 20.

Number of independent conversation repetitions for each test case. Defaults to 1 when not specified at either the data-source or test-case level.

Target number of turns in each conversation. The effective value cannot exceed the effective max_num_turns. When omitted, no target is set and the simulation model determines the conversation length dynamically from the scenario.

Configures effects applied to simulated conversation audio.

Effects to apply to simulated audio.

Volume of the configured audio effects, as a percentage from 1 through 100. When omitted, the service defaults to 15.

Configures conversation behavior for the simulated user.

Configuration for simulated user interruption behavior.

Configures the default interruption behavior for the simulated user.

The interruption type, always default.

Evaluator references used to score candidate quality.

Configures the model that plays the simulated user. This model is separate from the evaluated target.

Model deployment that generates simulated user turns, in the format {connectionName}/modelDeploymentName.

Represents a set of parameters used to control the sampling behavior of a language model during text generation.

The temperature parameter for sampling. Defaults to 1.0.

The top-p parameter for nucleus sampling. Defaults to 1.0.

The random seed for reproducibility. Defaults to 42.

The maximum number of tokens allowed in the completion.

Voice configuration used to convert text to speech for evaluation through the Voice Live endpoint.

Configures an Azure standard neural voice used to convert text to speech for evaluation through the Voice Live endpoint.

The voice kind. Always azure-standard.

The Azure neural voice name.

The synthesis temperature, from 0 to 1. When omitted, the service defaults to the underlying voice model's default temperature.

Maximum number of target-agent runs executed concurrently during each single-turn evaluation. If omitted, the service defaults to 1. Conversation evaluation supports only 1.

Candidate search settings.

Maximum number of non-baseline candidates to fully evaluate. If omitted, the service defaults to 1.

Caller-supplied baseline agent values that cannot be resolved from the target agent.

Caller-supplied baseline system prompt. Omitted to resolve the baseline from the target; a prompt agent's stored definition is authoritative.

A non-empty Foundry model name or existing deployment name.

Skills available for optimization. Omitted when no manual skill surface is supplied.

Function tools available for optimization. Omitted when no manual tool surface is supplied. The optimizer may change function and argument descriptions while preserving names and parameter JSON Schema.

The agent attributes and alternative values available to an agent-optimization search.

Agent attributes the optimizer may change. If omitted, the service defaults to instructions.

Alternative model names or existing deployment names available when model is an optimization target.

Configuration for a one-shot prompt transformation without comparative evaluation.

Optimization type discriminator.

Context used to steer prompt optimization. Omitted when no context is supplied.

A duration encoded as an integer number of milliseconds.

Non-fatal warnings emitted during optimization. Omitted when no warnings were produced.

The public explanation for this block.

The public continuation instruction.

The public explanation for this block.

The public continuation instruction.