Mastering AI AgentOps: Building, Evaluating, and Monitoring AI Agents

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Introduction

Artificial Intelligence (AI) agents are revolutionizing automation, but managing, evaluating, and optimizing them efficiently is a challenge. AgentOps is a powerful tool designed to help developers build, debug, and monitor AI agents from prototype to production.

This blog explores AgentOps' key features, installation, and practical applications using Python. By the end, you'll know how to:

• Set up AgentOps in your projects
• Monitor AI agent sessions and track costs, actions, and errors
• Debug and analyze execution flows for enhanced agent performance
• Implement multi-agent support with OpenAI and Gemini models

Let's dive in!

🚀 Getting Started with AgentOps

1️⃣ Installation & Setup

To begin, install AgentOps and LangChain:

!pip install agentops langchain_community

Next, set up API keys for OpenAI, Gemini, and AgentOps:

from google.colab import userdata
import os

OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')
GEMINI_API_KEY = userdata.get('GOOGLE_API_KEY')
AGENTOPS_API_KEY = userdata.get('AGENTOPS_API_KEY')

2️⃣ Initialize OpenAI & AgentOps

from openai import OpenAI
import agentops

openai = OpenAI(api_key=OPENAI_API_KEY)
agentops.init(AGENTOPS_API_KEY, default_tags=["openai-gpt-notebook"])

👉 Why is this important?

• This initializes AgentOps for tracking AI agent sessions and activities.
• It ensures seamless interaction between AgentOps and LLM providers like OpenAI and Gemini.

🔍 Analyzing AI Agent Behavior with AgentOps

3️⃣ Tracking AI Agent Execution

Monitor agent activity and debug interactions with step-by-step execution graphs:

message = [{"role": "user", "content": "Write a 12-word poem about secret agents."}]
response = openai.chat.completions.create(
    model="gpt-3.5-turbo", messages=message, temperature=0.5, stream=False
)
print(response.choices[0].message.content)

✅ Expected Output:

Silent shadows move, secrets kept in the dark, agents unseen, unknown.

Key Features:

• Session Stats: Tracks execution duration, LLM usage, and error count.
• Session Replay: Generates a unique link to visualize agent interactions.

agentops.end_session("Success")

📌 Use Case: Debugging AI-generated responses, verifying output correctness.

💡 Cost & Compliance Tracking

4️⃣ Managing LLM Costs and Security

AgentOps helps developers track AI agent costs and prevent security risks like prompt injections.

agentops.start_session(tags=["openai-gpt-notebook-events"])

Session Replay Link: 🖇 AgentOps Session Replay

agentops.end_session("Success")

Key Benefits:

• Monitor model usage and expenses
• Detect security vulnerabilities (prompt injections, data leaks)
• Ensure compliance with AI best practices

📌 Use Case: Budget tracking for AI-powered applications.

🤖 Multi-Agent Support with AgentOps

5️⃣ Implementing Multi-Agent Workflows

AgentOps can track interactions between multiple AI agents, such as a QA agent and a Software Engineer agent.

Define AI Agents:

from agentops import track_agent

@track_agent(name="qa")
class QaAgent:
    def completion(self, prompt: str):
        res = openai.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "system", "content": "You are a QA engineer and only output Python code."},
                      {"role": "user", "content": prompt}],
            temperature=0.5,
        )
        return res.choices[0].message.content

@track_agent(name="engineer")
class EngineerAgent:
    def completion(self, prompt: str):
        res = openai.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "system", "content": "You are a software engineer and only output Python code."},
                      {"role": "user", "content": prompt}],
            temperature=0.5,
        )
        return res.choices[0].message.content

Initialize Agents & Generate Code

qa = QaAgent()
engineer = EngineerAgent()

generated_func = engineer.completion("Python function to test prime number")
print(generated_func)

✅ Expected Output:

def is_prime(num):
    if num < 2:
        return False
    for i in range(2, int(num ** 0.5) + 1):
        if num % i == 0:
            return False
    return True

📌 Use Case: AI-powered code generation and automated QA testing.

🔄 AI Model Integration: OpenAI & Gemini

6️⃣ Gemini Model Integration

AgentOps supports Google's Gemini model for AI workflows:

import google.generativeai as genai

genai.configure(api_key=GEMINI_API_KEY)
agentops.start_session(AGENTOPS_API_KEY)
model = genai.GenerativeModel("gemini-1.5-flash")

🔹 Test Synchronous Generation:

response = model.generate_content("What are the three laws of robotics?")
print(response.text)

✅ Expected Output:

1. A robot may not injure a human being... 2. A robot must obey the orders given... 3. A robot must protect its own existence...

   📌 Use Case: AI-driven conversational agents using multiple LLMs.

🎯 Conclusion

AgentOps is an essential tool for AI developers, offering:

• Robust AI agent monitoring & debugging
• Cost tracking & security compliance
• Multi-agent workflow support
• Seamless OpenAI & Gemini integrations

By incorporating AgentOps into your workflow, you can optimize, debug, and scale AI agents effectively.

📖 Next Steps:

• Explore AgentOps Documentation
• Try integrating AgentOps with LangChain
• Experiment with custom AI agent monitoring solutions
• AgentOps Notebook

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