Watch how a Pro develops AI Agents in real-time

Introduction

In this article, we will dive into the process of developing AI agents using AgentOps, a powerful platform that helps with agent testing, monitoring, and evaluation. We'll follow along as a professional developer builds an AI agent capable of web scraping and summarizing information from websites. We'll explore the AgentOps dashboard, which provides detailed insights into agent performance, cost, and execution time. Let's get started and witness the development of AI agents in action!

Step 1: Building the Agent

The first step is to create a Python script that defines the functions required for web scraping and text summarization. We'll leverage the firecrawl library for web scraping and the openai library for text summarization using large language models (LLMs).

The crawl_web function utilizes the firecrawl library to scrape a given website and returns the scraped data. We set up the necessary headers and API key before making the request and extracting the JSON response. Here's an overview of the code snippet for the crawl_web function:

import requests
import json
import agentops

def crawl_web(url):
    headers = ("Content-Type": "application/json")
    api_key = agentops.getenv("API_KEY")
    body = (
        "url": url
    )
    response = requests.post(url, headers=headers, json=body)
  
    if response.status_code == 200:
        return response.json()
    else:
        return "Scraping failed"

Next, we define the summarize_text function that takes the web data obtained from the crawl_web function and uses the openai library to summarize the text. We set up an OpenAI client, define the prompt, and make an API call to generate the text summary. Here's a snippet of the code for the summarize_text function:

import openai

def summarize_text(web_data):
    client = openai.Client(api_key='YOUR_API_KEY')
    messages = [
        ("role": "system", "content": "Your prompt to summarize the text."),
        ("role": "user", "content": web_data)
    ]
    response = client.complete(
        engine="davinci",
        messages=messages
    )
    return response.choices[0].text.strip()

Finally, we create the main function that initiates the AgentOps session, calls the web scraping function, and then the text summarization function. We also add the agentops.record_function decorator to track the function calls and their results. Here's an outline of the code for the main function:

import agentops

@agentops.record_function
def main():
    agentops.init()
    url = "https://example.com"
    web_data = crawl_web(url)
    summarized_text = summarize_text(web_data)
    print(summarized_text)
    agentops.end_session()

if __name__ == "__main__":
    main()

Step 2: Monitoring with AgentOps Dashboard

Now that we have our agent script ready, let's leverage the powerful capabilities of the AgentOps dashboard to monitor our agent's performance. By importing the agentops module and initializing the session, we can track and analyze various metrics related to cost, execution time, and errors.

We can view the live session in the AgentOps dashboard by clicking on the provided session link. The dashboard provides an in-depth overview of the agent's actions, including the chat history, tool calls, and LLN utilizations. We can easily identify errors and interruptions that occurred during the execution.

Within the dashboard, we can also access the session breakdown, which gives a comprehensive summary of all agents' performance over a specific duration. This view helps to identify agents that have failed or encountered issues, such as 429 or 400 error codes. Furthermore, cost breakdown analysis provides insights into the financial aspects of running the agents.

Keywords

• AI agent development
• AgentOps
• Web scraping
• Text summarization
• Agent monitoring
• Agent performance
• Agent cost analysis

Step 3: FAQ

Q: What is AgentOps? AgentOps is a platform that assists in the development, testing, monitoring, and evaluation of AI agents. It provides a comprehensive dashboard to track agent performance, cost, and execution time.

Q: How does AgentOps help in building AI agents? AgentOps offers features like agent testing, monitoring, and debugging. It provides real-time insights into the agents' behavior, execution time, and costs, allowing developers to optimize their code and improve agent performance.

Q: Can I track the cost of running AI agents using AgentOps? Yes, AgentOps allows you to monitor the cost of running AI agents by providing detailed breakdowns of the financial aspects associated with each agent's execution.

Q: How can I analyze the performance of my AI agents with AgentOps? The AgentOps dashboard offers visualizations and metrics to analyze agent performance, including chat history, tool calls, and LLN utilization. It helps identify errors, interruptions, and areas for improvement in AI agent development.

Q: What integrations does AgentOps support? AgentOps supports various agent frameworks, including Crew AI, Autogen, and LAMA Index. These integrations ensure seamless compatibility with different development environments and frameworks.

Q: Can I test my AI agents against specific evaluation sets using AgentOps? Yes, AgentOps provides the capability to run agents against evaluation sets like Web Arena, enabling developers to evaluate their agent's performance and address any shortcomings.

Q: How can I get support while using AgentOps? The AgentOps community offers support through a dedicated Discord server, where you can connect with other developers, ask questions, and seek assistance in debugging and optimizing your AI agents.

Q: Is AgentOps suitable for production-scale agent deployment? Yes, AgentOps is designed to facilitate the transition from development to production-scale agent deployment. Its monitoring capabilities and cost analysis help ensure reliable and cost-effective agent operations.

Q: Can I track the progress of my AI agent development over time with AgentOps? Yes, the AgentOps dashboard provides a session history and breakdown, allowing you to visualize your progress, track agent performance, and identify areas where improvements have been made.

Q: How easy is it to integrate AgentOps into my agent development workflow? Integrating AgentOps into your agent development workflow is straightforward. With just two lines of code, you can import the agentops module and initiate the session. From there, you gain access to a powerful monitoring and evaluation dashboard.

Conclusion

AgentOps is an invaluable tool for developing, testing, monitoring, and evaluating AI agents. With its intuitive dashboard, developers can gain real-time insights into their agents' performance, cost, and execution. By following along with the agent development process, we've seen how AgentOps simplifies tracking, debugging, and optimizing AI agents. Whether you're a seasoned professional or just starting with AI agent development, AgentOps can help you build reliable and efficient agents that can have a tangible impact.