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User confirmation allows you to pause execution and require explicit user approval before proceeding with tool calls. This is useful for:
  • Sensitive operations
  • API calls that modify data
  • Actions with significant consequences

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How It Works

When you mark a tool with requires_confirmation=True, your agent will:
  1. Pause execution when the tool is about to be called
  2. Set is_paused to True on the run response
  3. Wait for you to review the tool call and decide whether to approve or reject it
  4. Continue execution once you call continue_run() with your decision
This gives you complete control over which tools execute and when, making it perfect for production scenarios where you need human oversight.

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Basic Example

The following example shows how to implement user confirmation with a custom tool: 
from agno.tools import tool
from agno.agent import Agent
from agno.models.openai import OpenAIChat

@tool(requires_confirmation=True)
def sensitive_operation(data: str) -> str:
    """Perform a sensitive operation that requires confirmation."""
    # Implementation here
    return "Operation completed"

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[sensitive_operation],
)

# Run the agent
run_response = agent.run("Perform sensitive operation")

# Handle confirmation
if run_response.is_paused:
    for tool in run_response.tools_requiring_confirmation:
        # Get user confirmation
        print(f"Tool {tool.tool_name}({tool.tool_args}) requires confirmation")
        confirmed = input(f"Confirm? (y/n): ").lower() == "y"
        tool.confirmed = confirmed

    # Continue execution
    response = agent.continue_run(run_id=run_response.run_id, updated_tools=run_response.tools)

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Toolkit-Level Confirmation

You can also specify which specific tools in a toolkit require confirmation using the requires_confirmation_tools parameter. This is super useful when you want to protect only certain operations in a toolkit while allowing others to run freely: 
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools import tool
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.utils import pprint
from rich.console import Console
from rich.prompt import Prompt

console = Console()

agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    tools=[DuckDuckGoTools(requires_confirmation_tools=["duckduckgo_search"])],
    markdown=True,
)

run_response = agent.run("Search for the latest news on Apple?")
if run_response.is_paused:
    for tool in run_response.tools_requiring_confirmation:  # type: ignore
        # Ask for confirmation
        console.print(
            f"Tool name [bold blue]{tool.tool_name}({tool.tool_args})[/] requires confirmation."
        )
        message = (
            Prompt.ask("Do you want to continue?", choices=["y", "n"], default="y")
            .strip()
            .lower()
        )

        if message == "n":
            tool.confirmed = False
        else:
            # Update the tools in place
            tool.confirmed = True

    run_response = agent.continue_run(run_id=run_response.run_id, updated_tools=run_response.tools)
    pprint.pprint_run_response(run_response)

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Providing Rejection Feedback

When rejecting a tool call, you can provide feedback to the agent using the confirmation_note property. This helps the agent understand why the operation was rejected and potentially choose a better approach: 
if run_response.is_paused:
    for tool in run_response.tools_requiring_confirmation:
        print(f"Tool {tool.tool_name}({tool.tool_args}) requires confirmation")
        confirmed = input(f"Confirm? (y/n): ").lower() == "y"
        
        if confirmed:
            tool.confirmed = True
        else:
            tool.confirmed = False
            tool.confirmation_note = "This operation was rejected because it targets the wrong resource. Please use the alternative method."

    response = agent.continue_run(run_id=run_response.run_id, updated_tools=run_response.tools)

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Mixed Tool Scenarios

You can mix tools that require confirmation with tools that don’t. The agent will execute the non-confirmation tools automatically and only pause for those that need approval: 
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools import tool

def safe_operation() -> str:
    """This runs automatically without confirmation."""
    return "Safe operation completed"

@tool(requires_confirmation=True)
def risky_operation() -> str:
    """This requires user confirmation."""
    return "Risky operation completed"

agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    tools=[safe_operation, risky_operation],
)

run_response = agent.run("Perform both operations")

if run_response.is_paused:
    # Only the risky_operation will be in tools_requiring_confirmation
    for tool in run_response.tools_requiring_confirmation:
        # Handle confirmation...
        tool.confirmed = True
    
    response = agent.continue_run(run_id=run_response.run_id, updated_tools=run_response.tools)

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Async Support

User confirmation works seamlessly with async agents. Just use arun() and acontinue_run(): 
run_response = await agent.arun("Perform sensitive operation")

if run_response.is_paused:
    for tool in run_response.tools_requiring_confirmation:
        tool.confirmed = True
    
    response = await agent.acontinue_run(run_response=run_response)

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Streaming Support

User confirmation also works with streaming responses. The agent will pause mid-stream when it encounters a tool that requires confirmation: 
for run_event in agent.run("Perform sensitive operation", stream=True):
    if run_event.is_paused:
        for tool in run_event.tools_requiring_confirmation:
            tool.confirmed = True
        
        # Continue streaming
        response = agent.continue_run(
            run_id=run_event.run_id,
            updated_tools=run_event.tools,
            stream=True
        )

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Usage Examples

Basic Confirmation

Simple user confirmation flow

Async Confirmation

Using confirmation with async agents

Mixed Tools

Combining confirmation and non-confirmation tools

Multiple Tools

Handling multiple confirmations

Streaming Async

Confirmation with streaming responses

Toolkit Confirmation

Using confirmation with toolkits

With History

Confirmation with chat history

With Run ID

Resume confirmation using run_id

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Developer Resources