videointermediate

I Built an Async AI Agent 🤯 | Stocks + Translate + Website Check (AutoGen Python)

By Nidhi ChouhanApril 27, 2026youtube

This tutorial demonstrates building an asynchronous AI agent using AutoGen in Python that can handle multiple concurrent tasks including stock price lookups, text translation, and website availability checks. The agent leverages AutoGen's multi-agent framework to coordinate different specialized agents that work together to process requests efficiently. The implementation showcases practical patterns for creating scalable, task-specific agents that communicate and collaborate within a single system.

Key Points

•Use AutoGen framework to create multiple specialized agents (stock agent, translator agent, website checker agent) that handle specific domains
•Implement asynchronous execution patterns to allow agents to process multiple tasks concurrently without blocking
•Design agent communication protocols where agents can delegate tasks and share results with a coordinator/manager agent
•Integrate external APIs and tools (stock data APIs, translation services, HTTP requests) into agent capabilities
•Structure agents with clear system prompts that define their role, capabilities, and constraints
•Use agent composition to build complex workflows where simple agents combine to solve multi-faceted problems
•Implement error handling and validation for external API calls within agent logic
•Test agent interactions and message passing to ensure reliable task completion and result aggregation

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Workflow Diagram

Start Process

Step A

Step B

Step C

Complete

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Skills & Tools Multi-Agent Systems Agent Teams Tool Use

Artifacts (2)

AutoGen Multi-Agent Setuppythonscript

# Example AutoGen multi-agent structure
from autogen import AssistantAgent, UserProxyAgent

# Define specialized agents
stock_agent = AssistantAgent(
    name="StockAgent",
    system_message="You are a stock market expert. Fetch and analyze stock prices.",
    llm_config={"model": "gpt-4"}
)

translator_agent = AssistantAgent(
    name="TranslatorAgent",
    system_message="You are a translation expert. Translate text between languages.",
    llm_config={"model": "gpt-4"}
)

website_agent = AssistantAgent(
    name="WebsiteAgent",
    system_message="You are a website monitoring expert. Check website availability and status.",
    llm_config={"model": "gpt-4"}
)

manager_agent = UserProxyAgent(
    name="Manager",
    human_input_mode="NEVER",
    code_execution_enabled=True
)

# Initiate multi-agent conversation
manager_agent.initiate_chat(
    stock_agent,
    message="Get the current price of AAPL stock"
)

Async Task Execution Patternpythonscript

import asyncio
from typing import List, Dict

class AsyncAgentCoordinator:
    def __init__(self, agents: Dict):
        self.agents = agents
    
    async def execute_task(self, task_type: str, payload: str) -> str:
        """Route task to appropriate agent asynchronously"""
        if task_type == "stock":
            return await self.agents["stock"].process(payload)
        elif task_type == "translate":
            return await self.agents["translator"].process(payload)
        elif task_type == "website":
            return await self.agents["website"].process(payload)
    
    async def execute_multiple_tasks(self, tasks: List[Dict]) -> List[str]:
        """Execute multiple tasks concurrently"""
        results = await asyncio.gather(
            *[self.execute_task(t["type"], t["payload"]) for t in tasks]
        )
        return results

# Usage
coordinator = AsyncAgentCoordinator(agents_dict)
tasks = [
    {"type": "stock", "payload": "AAPL"},
    {"type": "translate", "payload": "Hello world"},
    {"type": "website", "payload": "https://example.com"}
]
results = asyncio.run(coordinator.execute_multiple_tasks(tasks))