Update Agent

bedrockagent_update_agent

R Documentation

Updates the configuration of an agent

Description

Updates the configuration of an agent.

Usage

bedrockagent_update_agent(agentCollaboration, agentId, agentName,
  agentResourceRoleArn, customOrchestration, customerEncryptionKeyArn,
  description, foundationModel, guardrailConfiguration,
  idleSessionTTLInSeconds, instruction, memoryConfiguration,
  orchestrationType, promptOverrideConfiguration)

Arguments

agentCollaboration

The agent's collaboration role.

agentId

[required] The unique identifier of the agent.

agentName

[required] Specifies a new name for the agent.

agentResourceRoleArn

[required] The Amazon Resource Name (ARN) of the IAM role with permissions to invoke API operations on the agent.

customOrchestration

Contains details of the custom orchestration configured for the agent.

customerEncryptionKeyArn

The Amazon Resource Name (ARN) of the KMS key with which to encrypt the agent.

description

Specifies a new description of the agent.

foundationModel

[required] The identifier for the model that you want to be used for orchestration by the agent you create.

The modelId to provide depends on the type of model or throughput that you use:

• If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see Amazon Bedrock base model IDs (on-demand throughput) in the Amazon Bedrock User Guide.

• If you use an inference profile, specify the inference profile ID or its ARN. For a list of inference profile IDs, see Supported Regions and models for cross-region inference in the Amazon Bedrock User Guide.

• If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see Run inference using a Provisioned Throughput in the Amazon Bedrock User Guide.

• If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.

• If you use an imported model, specify the ARN of the imported model. You can get the model ARN from a successful call to CreateModelImportJob or from the Imported models page in the Amazon Bedrock console.

guardrailConfiguration

The unique Guardrail configuration assigned to the agent when it is updated.

idleSessionTTLInSeconds

The number of seconds for which Amazon Bedrock keeps information about a user's conversation with the agent.

A user interaction remains active for the amount of time specified. If no conversation occurs during this time, the session expires and Amazon Bedrock deletes any data provided before the timeout.

instruction

Specifies new instructions that tell the agent what it should do and how it should interact with users.

memoryConfiguration

Specifies the new memory configuration for the agent.

orchestrationType

Specifies the type of orchestration strategy for the agent. This is set to DEFAULT orchestration type, by default.

promptOverrideConfiguration

Contains configurations to override prompts in different parts of an agent sequence. For more information, see Advanced prompts.

Value

A list with the following syntax:

list(
  agent = list(
    agentArn = "string",
    agentCollaboration = "SUPERVISOR"|"SUPERVISOR_ROUTER"|"DISABLED",
    agentId = "string",
    agentName = "string",
    agentResourceRoleArn = "string",
    agentStatus = "CREATING"|"PREPARING"|"PREPARED"|"NOT_PREPARED"|"DELETING"|"FAILED"|"VERSIONING"|"UPDATING",
    agentVersion = "string",
    clientToken = "string",
    createdAt = as.POSIXct(
      "2015-01-01"
    ),
    customOrchestration = list(
      executor = list(
        lambda = "string"
      )
    ),
    customerEncryptionKeyArn = "string",
    description = "string",
    failureReasons = list(
      "string"
    ),
    foundationModel = "string",
    guardrailConfiguration = list(
      guardrailIdentifier = "string",
      guardrailVersion = "string"
    ),
    idleSessionTTLInSeconds = 123,
    instruction = "string",
    memoryConfiguration = list(
      enabledMemoryTypes = list(
        "SESSION_SUMMARY"
      ),
      sessionSummaryConfiguration = list(
        maxRecentSessions = 123
      ),
      storageDays = 123
    ),
    orchestrationType = "DEFAULT"|"CUSTOM_ORCHESTRATION",
    preparedAt = as.POSIXct(
      "2015-01-01"
    ),
    promptOverrideConfiguration = list(
      overrideLambda = "string",
      promptConfigurations = list(
        list(
          basePromptTemplate = "string",
          foundationModel = "string",
          inferenceConfiguration = list(
            maximumLength = 123,
            stopSequences = list(
              "string"
            ),
            temperature = 123.0,
            topK = 123,
            topP = 123.0
          ),
          parserMode = "DEFAULT"|"OVERRIDDEN",
          promptCreationMode = "DEFAULT"|"OVERRIDDEN",
          promptState = "ENABLED"|"DISABLED",
          promptType = "PRE_PROCESSING"|"ORCHESTRATION"|"POST_PROCESSING"|"KNOWLEDGE_BASE_RESPONSE_GENERATION"|"MEMORY_SUMMARIZATION"
        )
      )
    ),
    recommendedActions = list(
      "string"
    ),
    updatedAt = as.POSIXct(
      "2015-01-01"
    )
  )
)

Request syntax

svc$update_agent(
  agentCollaboration = "SUPERVISOR"|"SUPERVISOR_ROUTER"|"DISABLED",
  agentId = "string",
  agentName = "string",
  agentResourceRoleArn = "string",
  customOrchestration = list(
    executor = list(
      lambda = "string"
    )
  ),
  customerEncryptionKeyArn = "string",
  description = "string",
  foundationModel = "string",
  guardrailConfiguration = list(
    guardrailIdentifier = "string",
    guardrailVersion = "string"
  ),
  idleSessionTTLInSeconds = 123,
  instruction = "string",
  memoryConfiguration = list(
    enabledMemoryTypes = list(
      "SESSION_SUMMARY"
    ),
    sessionSummaryConfiguration = list(
      maxRecentSessions = 123
    ),
    storageDays = 123
  ),
  orchestrationType = "DEFAULT"|"CUSTOM_ORCHESTRATION",
  promptOverrideConfiguration = list(
    overrideLambda = "string",
    promptConfigurations = list(
      list(
        basePromptTemplate = "string",
        foundationModel = "string",
        inferenceConfiguration = list(
          maximumLength = 123,
          stopSequences = list(
            "string"
          ),
          temperature = 123.0,
          topK = 123,
          topP = 123.0
        ),
        parserMode = "DEFAULT"|"OVERRIDDEN",
        promptCreationMode = "DEFAULT"|"OVERRIDDEN",
        promptState = "ENABLED"|"DISABLED",
        promptType = "PRE_PROCESSING"|"ORCHESTRATION"|"POST_PROCESSING"|"KNOWLEDGE_BASE_RESPONSE_GENERATION"|"MEMORY_SUMMARIZATION"
      )
    )
  )
)