Developing, Evaluating, and Deploying Agentic Systems

Introduction

Agentic systems are AI-driven workflows designed to automate tasks and decision-making. Aegis provides a structured framework for developing, testing, and deploying these systems efficiently. This guide walks developers through:

• Building and testing agents locally
• Configuring and running agents in a development stack
• Evaluating performance using structured benchmarks
• Tracking agent performance over time
• Automating retraining and optimization

Why This Approach?

Traditional development processes often lack structure when dealing with AI agents. With Aegis, we take a systematic, data-driven approach that ensures:

• Reproducibility: Every agent’s behavior is well-defined and versioned.
• Scalability: Agents can be tested on small datasets before deploying at scale.
• Monitoring: Performance is tracked continuously to improve efficiency.
• Automation: Retraining and optimizations occur without manual intervention.

1️⃣ Developing Agents Locally

Step 1: Define and Test Agents

Developers begin by designing agent behaviors and testing prompts. This can be done using:

• Aegis YAML/JSON Configuration (Preferred for structured, repeatable development)
• Autogen Studio (Optional: for interactive agent testing before formalizing configurations)

Example agent configuration:

agents:
  - name: "SupportAssistant"
    role: "Customer Support AI"
    behavior:
      - listen to customer queries
      - retrieve relevant documentation
      - generate helpful responses

Test the agent in Autogen Studio or within a Python script:

from autogen import Agent
agent = Agent.load_from_file("my_agent.yaml")
response = agent.run("How do I reset my password?")
print(response)

Step 2: Configure the Development Stack

1. Export agent configurations if using Autogen Studio:

   autogen export my_agent.yaml

2. Start the local development environment:
   • Use Docker Compose to spin up required services (Airflow, PostgreSQL, Prometheus).
   • Run a local API server to interact with agents.

Example docker-compose.yaml:

version: '3'
services:
  postgres:
    image: postgres:16
    ports:
      - "5432:5432"
  airflow:
    image: apache/airflow:2.6.1
    ports:
      - "8080:8080"

Step 3: Run and Debug Agents on Sample Data

To ensure agents work correctly, run them on small sample datasets before full deployment.

import requests
 
def test_agent(task_id, query):
    payload = {"task_id": task_id, "query": query}
    response = requests.post("http://localhost:8000/webhook", json=payload)
    print(response.json())
 
test_agent("test-001", "What is the return policy?")

2️⃣ Evaluating Agent Performance on Larger Data

Key Evaluation Metrics

Automating Performance Evaluation with Airflow

We use Airflow DAGs to run structured evaluations over large datasets.

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime
import time
 
def evaluate_agent():
    start_time = time.time()
    # Simulated agent evaluation
    response = "Agent response example"
    latency = time.time() - start_time
    print(f"Latency: {latency}, Response: {response}")
 
dag = DAG("agent_evaluation", schedule_interval=None, start_date=datetime(2024, 3, 12))
 
evaluate_task = PythonOperator(
    task_id="evaluate_agent",
    python_callable=evaluate_agent,
    dag=dag,
)

3️⃣ Tracking Agent Performance Over Time

To track long-term performance trends, we integrate Prometheus + Grafana.

Metrics to Track

• Latency trends
• Success/Failure rates
• Common errors
• Accuracy over time

Expose Metrics via FastAPI

Modify the webhook handler to expose real-time metrics:

from fastapi import FastAPI
from prometheus_client import Counter, Histogram, generate_latest
 
app = FastAPI()
 
request_counter = Counter("agent_requests_total", "Total number of agent requests")
latency_histogram = Histogram("agent_latency_seconds", "Response time in seconds")
 
@app.get("/metrics")
def get_metrics():
    return generate_latest()
 
@app.post("/webhook")
async def receive_event():
    request_counter.inc()
    latency_histogram.observe(0.5)  # Simulated latency
    return {"status": "Webhook received"}

4️⃣ Automating Retraining and Optimization

We define an Airflow retraining pipeline to improve agent performance when needed.

Trigger Retraining When Accuracy Drops

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime
 
def retrain_model():
    print("Retraining agent model...")
 
dag = DAG("agent_retraining", schedule_interval="@daily", start_date=datetime(2024, 3, 12))
 
retrain_task = PythonOperator(
    task_id="retrain_agent_model",
    python_callable=retrain_model,
    dag=dag,
)

🚀 Summary & Next Steps

🚀 Next Steps:

1. Define agent roles and workflows using YAML/JSON.
2. Run the local development stack and debug sample queries.
3. Evaluate performance on larger datasets using Airflow DAGs.
4. Monitor long-term trends with Grafana + Prometheus.
5. Automate retraining to improve performance over time.