# Invoke an inline agent
<a name="inline-agent-invoke"></a>

**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](https://docs.aws.amazon.com//bedrock/latest/userguide/inline-agent-prereq.html).

To invoke an inline agent, send a [InvokeInlineAgent](https://docs.aws.amazon.com//bedrock/latest/APIReference/API_agent-runtime_InvokeInlineAgent.html) API request with an [Agents for Amazon Bedrock runtime endpoint](https://docs.aws.amazon.com/general/latest/gr/bedrock.html#bra-rt) 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](https://docs.aws.amazon.com//bedrock/latest/userguide/foundation-models-reference.html) 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\$1config | 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\$1tokens that are used for model reasoning, which are a subset of the output tokens. For more information, see [Enhance model responses with model reasoning](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-reasoning.html). | 

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
    }
}
```