Airflow Agents for Enterprise AI: Architecture & Integration

from airflow.decorators import dag, task
from datetime import datetime, timedelta
import os

default_args = {
  'owner': 'esaholic-ai-team',
  'depends_on_past': False,
  'email_on_failure': True,
  'retries': 3,
  'retry_delay': timedelta(minutes=2),
}

@dag(
  dag_id='esaholic_ai_agent_batch_enrichment',
  default_args=default_args,
  description='Orchestrates AI Agent document enrichment tasks at scale.',
  schedule_interval='@hourly',
  start_date=datetime(2026, 1, 1),
  catchup=False,
  tags=['ai-agent', 'llm', 'production']
)
def ai_agent_enrichment_dag():

  @task()
  def fetch_pending_documents() -> list:
      """Fetches pending document IDs for agent enrichment."""
      return [
          {"doc_id": "DOC-2026-101", "content": "Quarterly financial report summary for Q2 2026."},
          {"doc_id": "DOC-2026-102", "content": "HIPAA compliance audit logs for medical cloud infrastructure."}
      ]

  @task(execution_timeout=timedelta(minutes=5))
  def execute_ai_agent_analysis(doc: dict) -> dict:
      """Executes LLM Agent reasoning loop with retry protection."""
      # Simulated AI Agent execution
      extracted_entities = ["Financial", "HIPAA", "Cloud"] if "HIPAA" in doc["content"] else ["Financial", "Q2"]
      return {
          "doc_id": doc["doc_id"],
          "status": "ENRICHED",
          "entities": extracted_entities,
          "confidence_score": 0.96
      }

  @task()
  def persist_results_to_lake(results: list) -> None:
      """Persists agent results to enterprise data lake."""
      print(f"Successfully persisted {len(results)} enriched agent records to Lakehouse.")

  # Define DAG execution dependencies
  docs = fetch_pending_documents()
  enriched_docs = execute_ai_agent_analysis.expand(doc=docs)
  persist_results_to_lake(enriched_docs)

# Instantiate DAG
dag_instance = ai_agent_enrichment_dag()