Building AI agents

Amazon Nova models are optimized for building AI agents with Amazon Nova Act. The models provide improved tool use, better reasoning for multi-step tasks, enhanced ability to maintain context across complex agent workflows and support for remote MCP tools.

Create an agent

AI agents built with Nova can orchestrate multiple tool calls, maintain context across extended interactions and course-correct when needed. Extended thinking transforms agentic workflows by enabling systematic reasoning through complex goals. Consider using a planning framework SDK such as Strands Agents to make the planning and execution process of your agent systems more robust.

Agent design patterns

When designing agents with Nova:

Multi-tool agent example

tool_config = { 
    "tools": [ 
        { 
            "toolSpec": { 
                "name": "calculator", 
                "description": "Perform mathematical calculations", 
                "inputSchema": { 
                    "json": { 
                        "type": "object", 
                        "properties": { 
                            "expression": { 
                                "type": "string", 
                                "description": "Mathematical expression to evaluate" 
                            } 
                        }, 
                        "required": ["expression"] 
                    } 
                } 
            } 
        }, 
        { 
            "toolSpec": { 
                "name": "database_query", 
                "description": "Query financial database for historical data", 
                "inputSchema": { 
                    "json": { 
                        "type": "object", 
                        "properties": { 
                            "query": { 
                                "type": "string", 
                                "description": "SQL query to execute" 
                            } 
                        }, 
                        "required": ["query"] 
                    } 
                } 
            } 
        } 
    ] 
} 
 
response = client.converse( 
    modelId=" us.amazon.nova-2-lite-v1:0", 
    messages=[{ 
        "role": "user", 
        "content": [{ 
            "text": "Analyze our Q3 financial performance across all business units, calculate year-over-year growth rates with statistical significance testing, and recommend budget allocation strategies for Q4." 
        }] 
    }], 
    toolConfig=tool_config, 
    inferenceConfig={"maxTokens": 10000, "temperature": 1, “topP”: 0.9}, 
    additionalModelRequestFields={ 
        "reasoningConfig": { 
            "type": "enabled", 
            "maxReasoningEffort": "low" 
        } 
    } 
) 

Invoke an agent

Agent invocation involves managing the conversation flow, processing tool calls, and maintaining state across multiple interactions.

Streaming agent responses

Stream responses to provide real-time visibility into the agent's reasoning and actions:

import boto3
response = client.converse_stream( 
    modelId=" us.amazon.nova-2-lite-v1:0", 
    messages=[{ 
        "role": "user", 
        "content": [{ 
            "text": "Design a scalable microservices architecture for an e-commerce platform handling 1M+ daily transactions. Consider data consistency, fault tolerance, performance, security, and cost optimization." 
        }] 
    }], 
    inferenceConfig={"maxTokens": 10000, "temperature": 10}, 
    additionalModelRequestFields={ 
        "reasoningConfig": { 
            "type": "enabled", 
            "maxReasoningEffort": "low" 
        } 
    } 
) 
 
# Process the streaming response 
reasoning_complete = False 
for event in response["stream"]: 
    if "contentBlockDelta" in event: 
        delta = event["contentBlockDelta"]["delta"] 
         
        if "reasoningContent" in delta: 
            reasoning_text = delta["reasoningContent"]["reasoningText"]["text"] 
            print(f"{reasoning_text}", end="", flush=True) 
        elif "text" in delta: 
            if not reasoning_complete: 
                print(f" 
 
Final Architecture Design: 
") 
                reasoning_complete = True 
            print(f"{delta['text']}", end="", flush=True) 

Agent state management

Maintain conversation history and tool results to preserve context; the example below demonstrates this for a single turn, but the developer can determine how to orchestrate overall agent system based on workflow requirements. In addition, Amazon Web Services tools such as Strands manage agent context and tool state on behalf of the developer.

messages = []

messages = [] 
 
# Initial user query 
messages.append({ 
    "role": "user", 
    "content": [{"text": user_query}] 
}) 
 
# Get agent response 
response = client.converse( 
    modelId=" us.amazon.nova-2-lite-v1:0", 
    messages=messages, 
    toolConfig=tool_config, 
    inferenceConfig=inf_params 
) 
 
# Add assistant response to history 
messages.append(response["output"]["message"]) 
 
# Process tool calls and add results 
if response["stopReason"] == "tool_use": 
    tool = next( 
        block["toolUse"] 
        for block in response["output"]["message"]["content"] 
        if "toolUse" in block 
    ) 
     
    # Execute tool 
    result = execute_tool(tool["name"], tool["input"]) 
     
    # Add tool result to conversation 
    messages.append({ 
        "role": "user", 
        "content": [{ 
            "toolResult": { 
                "toolUseId": tool["toolUseId"], 
                "content": [{"json": result}], 
                "status": "success" 
            } 
        }] 
    }) 
     
    # Continue conversation 
    response = client.converse( 
        modelId=" us.amazon.nova-2-lite-v1:0", 
        messages=messages, 
        toolConfig=tool_config, 
        inferenceConfig=inf_params 
    ) 

Agent Best Practices

For more information about Agent Best Practices, see General best practices.

For guidance on developing Conversational AI agents, see Speech-to-Speech (Amazon Nova 2 Sonic).