Table of Contents
Last update: 2026-1-1
1. What is AI Agent
The emergence of AI agents marks a significant shift in the generative AI landscape. As these autonomous systems become more sophisticated, they have the potential to revolutionize various industries and transform the way we interact with technology. However, the development and deployment of AI agents also raise important questions about the ethical implications and potential risks associated with granting autonomy to AI systems.
One of the key challenges in the adoption of AI agents will be striking the right balance between harnessing their potential benefits and mitigating the risks. Companies will need to invest in robust governance frameworks and establish clear guidelines for the development and deployment of AI agents. This will require collaboration between industry leaders, policymakers, and researchers to ensure that the technology is developed responsibly and in line with ethical principles.
As AI agents become more prevalent, it will also be crucial to address the potential impact on the workforce. While these systems may automate certain tasks and improve efficiency, it is essential to consider the implications for job displacement and the need for reskilling and upskilling initiatives. Ultimately, the success of AI agents will depend on our ability to navigate these challenges and ensure that the technology is developed and deployed in a way that benefits society as a whole.
1.1 Driving Productivity, Cost Reduction, and Informed Decision-Making
- AI agents are rational agents that make optimal decisions based on perceptions and data.
- Businesses can delegate repetitive tasks to AI agents, allowing teams to focus on mission-critical activities.
- AI agents reduce costs by minimizing inefficiencies, human errors, and manual processes.
- Advanced AI agents use machine learning to process real-time data, enabling better predictions and informed decision-making.
- AI agents personalize experiences, provide prompt responses, and innovate to improve customer engagement, conversion, and loyalty.
2. How to build AI Agent
2.1 Building Intelligent Systems
Introduction:
In the rapidly evolving world of artificial intelligence, AI agents have emerged as a game-changing technology, revolutionizing various industries and transforming the way we interact with machines. This comprehensive guide will walk you through the essential steps and best practices for building powerful AI agents that can tackle complex tasks and deliver unparalleled results.
Understanding AI Agents:
Before diving into the building process, it’s crucial to grasp the fundamentals of AI agents. These intelligent systems are designed to perceive their environment, process information, and make decisions or take actions to achieve specific goals. AI agents can be categorized into different types, such as reactive, model-based, goal-oriented, and learning agents, each with its own unique characteristics and capabilities.
Defining the Problem and Goals:
The first step in building an AI agent is to clearly define the problem it will solve and the goals it should achieve. This involves understanding the domain, identifying the key challenges, and determining the desired outcomes. By establishing a well-defined problem statement and setting measurable goals, you lay the foundation for a focused and effective AI agent development process.
Choosing the Right Architecture:
Selecting the appropriate architecture is critical to the success of your AI agent. There are various architectures to choose from, such as rule-based systems, decision trees, neural networks, and reinforcement learning models. Each architecture has its strengths and weaknesses, and the choice depends on the nature of the problem, available data, and computational resources. It’s essential to evaluate the trade-offs and select the architecture that aligns best with your specific requirements.
Data Preparation and Preprocessing:
AI agents rely heavily on data to learn and make informed decisions. Therefore, data preparation and preprocessing are vital steps in the building process. This involves collecting relevant data, cleaning and normalizing it, and transforming it into a suitable format for training the AI agent. Data quality and diversity are key factors that impact the agent’s performance, so it’s important to ensure that the data is representative, unbiased, and covers a wide range of scenarios.
Training and Optimization:
Once the data is prepared, the next step is to train the AI agent using appropriate algorithms and techniques. This involves feeding the agent with labeled examples or letting it explore and learn from its interactions with the environment. The training process aims to optimize the agent’s performance by adjusting its internal parameters and refining its decision-making capabilities. Techniques such as supervised learning, unsupervised learning, and reinforcement learning are commonly used, depending on the nature of the problem and available data.
Testing and Evaluation:
After training, it’s crucial to thoroughly test and evaluate the AI agent’s performance. This involves exposing the agent to various scenarios, including edge cases and unseen data, to assess its robustness and generalization abilities. Evaluation metrics should be carefully chosen to measure the agent’s accuracy, efficiency, and effectiveness in achieving the desired goals. Iterative testing and refinement help identify and address any weaknesses or limitations in the agent’s behavior.
Deployment and Monitoring:
Once the AI agent has been successfully trained and evaluated, it’s ready for deployment in real-world environments. However, the work doesn’t stop there. Continuous monitoring and maintenance are essential to ensure the agent’s performance remains optimal over time. This involves tracking the agent’s decisions, analyzing its behavior, and making necessary updates or adjustments based on new data or changing requirements. Regular monitoring helps identify potential issues and enables timely interventions to maintain the agent’s effectiveness.
Conclusion:
Building AI agents is a complex and iterative process that requires careful planning, design, and execution. By following the steps outlined in this guide, you can create powerful and intelligent systems that can tackle a wide range of problems and deliver exceptional results. As AI continues to advance, the possibilities for AI agents are endless, and their impact on various domains will only continue to grow. Embrace the power of AI agents and unlock new frontiers in intelligent system development.
2.2 API
2.2.1 OpenAI API
Office website: OpenAI API
Async OpenAI API Code example:
import openai
from openai import OpenAI
from openai import AsyncOpenAI
async def __aenter__(self):
self.async_client_openai = AsyncAzureOpenAI(
api_key=os.environ['old_AZURE_OPENAI_KEY'],
api_version=os.environ['OPENAI_VERSION'],
azure_endpoint=os.environ['old_AZURE_OPENAI_ENDPOINT']
)
async with AsyncClients() as clients:
res = await clients.async_client_openai2.chat.completions.create(
model=openai_model,
max_tokens=4096,
temperature=0.2,
stream=False,
messages=conversation
)
assistant_content = res.choices[0].message.content
conversation.append({"role": "assistant", "content": assistant_content})OpenAI API is recently reported has blocked several regions including China. Those regions will stop access API since the beginning of Jul 2024.
2.2.2 Claude API
Office website: Claude API
Async AWS bedrock Claude Code example:
from anthropic import AsyncAnthropic, AnthropicBedrock, BadRequestError, AsyncAnthropicBedrock
client = AsyncAnthropicBedrock()
res = await client.messages.create(
model=aws_model,
max_tokens=4096,
temperature=0.2,
system=system,
# system=f"{system}\ncode:###{key}",
messages=conversation
)
assistant_content = res.content[0].text
conversation.append({"role": "assistant", "content": assistant_content})2.2.3 Google Gemini API
Office website: Gemini API
Code example:
import os
import google.generativeai as genai
from dotenv import load_dotenv, find_dotenv
load_dotenv(find_dotenv())
genai.configure(api_key=os.environ["GEMINI_API_KEY"])
# models/gemini-1.0-pro
# models/gemini-1.0-pro-001
# models/gemini-1.0-pro-latest
# models/gemini-1.0-pro-vision-latest
# models/gemini-1.5-flash-latest
# models/gemini-1.5-pro-latest
# models/gemini-pro
# models/gemini-pro-vision
model = genai.GenerativeModel('gemini-1.5-flash-latest')
response = model.generate_content("At which position is the letter e in raspberry")
print(response.text)2.2.4 X.AI Grok API
Office website: Grok API
2.3 Network Access
For users in China engaging in or applying AI tools, an appropriate network environment setup is essential. Currently, the recommended network environment configuration method is WARP, especially the Zero-Trust solution. For details, please refer to:
《How to use Cloudflare WARP Zero-Trust 1.1.1.1》
3. How to use AI Agent
Our AI Agent is web-based and does not require users to install any apps or plugins.
https://orbitmoonalpha.com/ai-agent
3.1 Interface
We use a minimalist interface to handle conversations, avoiding the trend of using uniform UIs. To achieve this, we created an original set of interface processes.
3.2 Usage Example
Directly interact with AI-Agent for multi-turn conversations
Input URL as reference material to pass in conversation history, ask multiple questions based on the reference material
Summarize YouTube video summaries, requires enabling subtitles for videos
Summarize and follow up on PDF files
Summarize and follow up on news or web articles
Analyze and ask questions about images
Generate high-quality images
3.3 Pay to upgrade
Upgrade AI Agent at out shop
4. AI Trend
4.1 Leading Model
4.1.1 OpenAI
4.1.2 Gemini
Original statement: This article is original content published by OMA on orbitmoonalpha.com. Please indicate the source when reprinting.