Invoke an inline agent

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Invoke an inline agent - Amazon Bedrock

Note

Configuring and invoking an inline agent feature is in preview release for Amazon Bedrock and is subject to change.

Before you invoke your inline agent, make sure you've completed the Prerequisites.

To invoke an inline agent, send a InvokeInlineAgent API request with an Agents for Amazon Bedrock runtime endpoint and minimally include the following fields.

Field	Use case
instruction	Provide instructions that tell the inline agent what it should do and how it should interact with users.
foundationModel	Specify a foundation model to use for orchestration by the inline agent you create. For example, anthropic claude, meta Llama3.1, etc.
sessionId	An unique identifier of the session. Use the same value across requests to continue same conversation.

The following fields are optional:

Field	Use case
actionGroups	List of action groups with each action group defining the actions that the inline agent can carry out.
knowledgeBases	Knowledge base associations with inline agent to augment response generated by the model.
guardrailConfiguration	Guardrail configurations to block topics, to prevent hallucinations, and to implement safeguards for your application.
agentCollaboration	Defines how the collaborator agent handles information across multiple collaborator agents to coordinate a final response. The collaborator agent can also be the supervisor.
collaboratorConfigurations	Configurations for collaborator agent.
collaborators	List of collaborator agents.
promptOverrideConfiguration	Configurations for advanced prompts used to override the default prompts.
enableTrace	Specify whether to turn on the trace or not to track the inline agent's reasoning process.
idleSessionTTLInSeconds	Specify the duration after which the inline agent should end the session and delete any stored information.
customerEncryptionKeyArn	Specify the ARN of a KMS key to encrypt agent resources,
endSession	Specify whether to end the session with the inline agent or not.
inlineSessionState	Parameters that specify the various attributes of a sessions.
inputText	Specify the prompt text to send to the agent.
reasoning_config	To enable model reasoning so that the model explains how it reached its conclusions. Use inside of a `additionalModelRequestFields` field. You must specify the number of `budget_tokens` that are used for model reasoning, which are a subset of the output tokens. For more information, see Enhance model responses with model reasoning.

The following InvokeInlineAgent API example provides complete inline agent configurations including the foundation model, instructions, action groups with code interpreter, guardrails, and knowledge bases.


response = bedrock_agent_runtime.invoke_inline_agent(
    // Initialization parameters: cannot be changed for a conversation
    sessionId='uniqueSessionId',
    customerEncryptionKeyArn: String,
    
    // Input
    inputText="Hello, can you help me with a task?",
    endSession=False,
    enableTrace=True,
    
    // Agent configurations
    foundationModel='anthropic.claude-3-7-sonnet-20250219-v1:0',
    instruction="You are a helpful assistant...",
    actionGroups=[
        {
            'name': 'CodeInterpreterAction',
            'parentActionGroupSignature': 'AMAZON.CodeInterpreter'
        },
        {
            'actionGroupName': 'FetchDetails',
            'parentActionGroupSignature': '',
            "actionGroupExecutor": { ... },
            "apiSchema": { ... },
            "description": "string",
            "functionSchema": { ... }
        }
    ],
    knowledgeBases=[
        {
            knowledgeBaseId: "string",
            description: 'Use this KB to get all the info',
            retrievalConfiguration: { 
                vectorSearchConfiguration: { 
                    filter: { ... },
                    numberOfResults: number,
                    overrideSearchType: "string"
               }
            }
        }
    ],
    guardrailConfiguration={
        guardrailIdentifier: 'BlockEverything',
        gurardrailVersion: '1.0'
    },
    promptOverrideConfiguration: {...}
    
    // session properties: persisted throughout conversation
    inlineSessionState = {
        sessionAttributes = { 'key': 'value' },
        promptSessionAttributes = {k:v},
        returnControlInvocationResults = {...},
        invocationId = 'abc',
        files = {...},
    }
  }

You can include model reasoning parameters in the request. The following is an example of a single prompt that turns on model reasoning in the additionalModelRequestFields.


{
    "basePromptTemplate": " ... ",
    "inferenceConfiguration": {
        "stopSequences": [
            "</answer>"
        ]
    },
    "parserMode": "DEFAULT",
    "promptCreationMode": "DEFAULT",
    "promptState": "DISABLED",
    "promptType": "ORCHESTRATION",
    "additionalModelRequestFields":
    "reasoning_config": {
        "type": "enabled",
        "budget_tokens": 1024
    }
}