Building a Workflow Orchestration System with AI Agents

Intermediate

Constructing an orchestration system involves selecting suitable tools (like Airflow), defining tasks as code, and establishing data pipelines. AI agents are integrated as tasks or services within these pipelines.

For example, an AI pipeline for sentiment analysis might include data collection, preprocessing, model inference, and reporting steps, each managed by respective agents or scripts. Using orchestration APIs, workflows can be scheduled, monitored, and retried automatically.

Code snippets below illustrate a simple Python-based workflow integrating AI components:

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime

# Define AI agent tasks
def fetch_data(): pass

def preprocess_data(): pass

def run_model(): pass

def generate_report(): pass

# Define the DAG
with DAG('AI_Workflow', start_date=datetime(2023,1,1), schedule_interval='@daily') as dag:
    task1 = PythonOperator(task_id='fetch_data', python_callable=fetch_data)
    task2 = PythonOperator(task_id='preprocess_data', python_callable=preprocess_data)
    task3 = PythonOperator(task_id='run_model', python_callable=run_model)
    task4 = PythonOperator(task_id='generate_report', python_callable=generate_report)
    task1 >> task2 >> task3 >> task4

This demonstrates orchestrating AI tasks effectively.