What are AI Agents: How To Create a Based AI Agent

Key Takeaways

• AI agents handle complex, autonomous tasks beyond simple commands, showcasing advanced decision-making and adaptability.
• The Based AI Agent template by Coinbase and Replit provides an easy starting point for developers to build blockchain-enabled AI agents.
• AI based agents specifically integrate with blockchain, supporting crypto wallets and transactions.
• Securing API keys in development is crucial to protect the agent from unauthorized access.

Gone are the days when robotic voices were stiff and halting—think classic scenes from The Jetsons or sci-fi films where robots announced each action with a staccato, mechanical tone. Even in recent depictions, this style is likely a nod to nostalgia. 

Today, Artificial Intelligence (AI) has evolved far beyond simple, pre-programmed responses. For example, virtual assistants like Siri and Google Assistant now hold natural conversations, respond to complex questions, and remember context from previous interactions. Some tools even create human-sounding podcasts, making AI voices feel more lifelike than ever. 

This shift is only the beginning; this capability deepens further with AI agents. According to recent reports, AI will soon be able to use computers similarly to humans, retrieving and modifying documents, interacting with websites, and running programs. AI agents are designed to go beyond basic commands by autonomously gathering and analyzing data, allowing them to perform tasks, make decisions, and adapt in real-time.

The rise of AI agents reflects a growing interest across industries in customizing AI to perform tasks intelligently and independently. Finance, healthcare, and customer service companies are already rolling out these agents to streamline operations, engage users, and boost efficiency. AI agents are also available for blockchain and crypto. 

However, AI agents do not enter the human scene without risks and challenges. Recognizing these is the first step, leading to insights on using them effectively—or even more, creating them.

This article will explain the different types of AI agents, what an Based AI agent is, how to build one, and the opportunities and challenges that come with it.

What an AI Agent Is and How It Works

“An AI agent is an interactive computer program with predefined goals, capable of a wide range of uses on behalf of a user or another program. AI agents can potentially understand and learn from their environment, make decisions, take action, and even continuously improve with minimal human intervention.” 

This is achieved by integrating several core components that allow it to interact with data, interpret it within its environment, select appropriate responses, and communicate meaningfully to users. Additionally, AI agents can benefit from human feedback, enhancing their adaptability and performance.

By effectively combining these components, AI agents can perform various tasks, from simple question-answering to complex problem-solving.

Data Processing

• Input: Agents take in information, like text, voice, or images.
• Preprocessing: Data is cleaned and prepared for easy analysis.
• Feature extraction: Key information is pulled out from the processed data.
• Representation: The extracted details are turned into a format that the AI can work with, like numerical vectors.

Decision-Making Algorithms

• Rule-based systems: These use set rules to make quick decisions.
• Machine learning: Using supervised and reinforcement learning methods, agents improve by studying data patterns.
• Neural networks: These advanced models handle and learn from big data for more accurate decisions.

Natural Language Processing (NLP)

• Natural language understanding (NLU): The AI analyzes language to interpret emotions, intentions, and specific details.
• Natural language generation (NLG): The AI produces clear, human-like responses or summaries.

User Interface (UI)

• Text-based Interfaces: Includes chatbots and text-based assistants for direct communication.
• Voice-based interfaces: Uses voice recognition and speech technology for hands-free interactions.
• Visual interfaces: Uses graphical displays to show information and provide interactive elements for users.

The Unique Role of AI Agents: How They Differ From Chatbots and Virtual Assistants

It is important to note that AI agents differ from chatbots and virtual assistants. While all three process information, each has unique characteristics. AI agents hold a special place due to their advanced ability to operate autonomously, adapt to complex tasks, and continuously improve over time. This sets them apart from chatbots, which handle specific queries, and virtual assistants, which focus on assisting with personal tasks.

Additionally, modern chatbots now offer text and voice interactions, thanks to advancements like text-to-speech and speech recognition. While chatbots and virtual assistants share these capabilities, chatbots generally follow predefined scripts for specific tasks. 

Some chatbots have memory features to retain context across interactions, though this does not lead to continuous learning or improvement. In contrast, AI agents can autonomously adapt, make complex decisions, and improve over time based on real-world feedback, setting them apart regarding true autonomy and adaptability.

Types of AI Agents

The following 4 rules define an AI agent’s functionality: autonomy, perception, decision-making, and adaptability.

• Autonomy: This rule implies that an AI agent should function independently to accomplish tasks without needing constant input from a user.
• Perception: An AI agent can interpret data from its environment, whether through sensors, cameras, or other inputs.
• Decision-making: This involves an AI agent’s capacity to select appropriate actions to meet its goals.
• Adaptability: Adaptability refers to an AI agent’s ability to learn from new information or experiences, improving its responses over time.

These principles form the groundwork for designing AI agents and are widely accepted in the AI community to describe the essential capabilities that enable intelligent, agent-like behavior.

​​The following table provides examples of various types of AI agents. Each category reflects the agent’s functionality, adaptability, and level of autonomy, as well as the specific ways AI agents interact with their environment, make decisions, and process information.

AI agents can often combine multiple types, especially in complex systems that require diverse capabilities. For example, a goal-based agent may integrate learning capabilities, adapting its approach over time as it strives to achieve its goal.

Step-By-Step Guide To Building a Based AI Agent

An AI based agent is a template created by Coinbase, a leading cryptocurrency exchange and blockchain platform, and Replit , an AI-powered software development platform. 

This template equips AI agents with crypto and blockchain abilities, enabling various types—such as reflex agents, goal-based agents, or utility-based agents—to access blockchain features such as crypto wallets and on-chain interactions. 

With this setup, AI agents can autonomously execute transactions, manage assets, and handle tasks in decentralized finance and crypto automation. By using this template, developers can build AI agents that are:

• Autonomous: Able to make independent decisions and perform actions directly on the blockchain.
• Crypto-enabled: Equipped with crypto wallets and ready to interact with smart contracts.
• Versatile: Ideal for various applications, from decentralized finance and trading bots to other automated tasks.

According to Coinbase , users can follow the next 5 steps to create AI-based agents:

Step 1:  Development Environment Set-up

Users can start by creating a workspace on Replit. Forking the Based Agent template provides a personal project copy with all necessary tools and files to modify goals and make decision processes. New users may need to create a Replit account. This setup builds the foundation for adding blockchain and crypto functions.

Step 2: Get API Keys

With the development environment set up, users can move to blockchain integration. Connecting the agent to the blockchain allows it to access and interact with live on-chain data, which is critical for tasks like monitoring market conditions or verifying transactions. This connection enables the agent to leverage real-time information as it operates.

• Go to the CDP portal on Coinbase.
• Create a new project, generate an application programming interference (API) key, and save the key and private key.
• Go to the OpenAI Platform, sign up or log in, and navigate to the API keys section.
• Create a new API key and fund your account with $1-2 for testing purposes.

Caution: Do not commit your API keys publicly. Use Replit’s environment variables to keep them secure.

Step 3: Configure Your Environment

In this step, users secure their API keys in Replit to allow the agent to access them safely.

• Add “Secrets”: In the Replit project, click on Tools in the left sidebar, then select Secrets. Users should add their API keys as secrets here to keep them secure while enabling the agent to use them.

With the API keys configured, the agent is ready for secure blockchain access and interactions, including managing digital assets through its crypto wallet. This setup equips the agent with essential transaction capabilities, allowing it to hold, send, and receive digital funds on the blockchain as a crypto-enabled agent.

Step 4: Understand the Code Structure

To make the agent effective, it is essential to understand how the code is organized and how it uses blockchain functions. The Based Agent template includes two main files that set up the agent’s capabilities and operation modes:

• agents.py: This file contains the core functions for blockchain interactions. It initializes the agent with a wallet, allowing it to handle tasks like sending and receiving funds. Users can add new functions here to extend what the agent can do on the blockchain.
• run.py: This file defines how the agent operates. It includes three modes:

1. Chat mode: Allows users to interact with the agent via a command line.
2. Autonomous mode: Lets the agent run independently based on preset instructions.

Two-agent mode: Enables a conversation between OpenAI and the Based Agent.

By understanding this structure, users can see where to add custom functions and choose the right mode to suit their tasks, setting up the agent to operate with the blockchain as needed.

Step 5: Expand Your Agent’s Capabilities

In this step, users can add new functions and test the agent to ensure it works smoothly with blockchain tasks.

• Define and add new functions: Users can add new functions to the agents.py file to give the agent more abilities. For example, a new function might enable the agent to check market prices or send alerts.

• Link agent: After adding a function, link it to the Agent instance in agents.py so the agent can access and use it.

• Test and Fine-Tune: Run tests to observe the agent’s performance, ensuring all blockchain interactions work as intended. Testing helps identify areas for adjustment so users can improve the agent’s accuracy, autonomy, and response to blockchain conditions. Fine-tuning the agent prepares it for real-world tasks, whether automated trading, asset management, or other blockchain applications.

Conclusion

AI agents have advanced from basic programmed responses to intelligent systems capable of handling complex tasks independently. 

Designed for specific blockchain functions, AI based agents combine AI technology with crypto capabilities, enabling them to operate with crypto wallets, manage assets, and perform tasks like autonomous trading. 

Platforms such as Coinbase and Replit have created templates that make it easier for developers to create these blockchain-ready agents.

However, common mistakes—such as poor security for API keys, limited testing, and over-reliance on rule-based logic—can impact the effectiveness of these agents. 

By following structured steps, individuals can create reliable, adaptable Based AI agents capable of securely managing crypto tasks and interacting within the blockchain.

FAQs

About the Author

Lorena Nessi

Lorena Nessi is an award-winning journalist and media and technology expert. She is based in Oxfordshire, UK, and holds a PhD in Communication, Sociology, and Digital Cultures, as well as a Master’s degree in Globalization, Identity, and Technology. Lorena has lectured at prestigious institutions, including Fairleigh Dickinson University, Nottingham Trent University, and the University of Oxford. Her journalism career includes working for the BBC in London and producing television content in Mexico and Japan. She has published extensively on digital cultures, social media, technology, and capitalism. Lorena is interested in exploring how digital innovation impacts cultural and social dynamics and has a keen interest in blockchain technology. In her free time, Lorena enjoys science fiction books and films, board games, and thrilling adventures that get her heart racing. A perfect day for her includes a spa session and a good family meal.

See more

[email protected] LinkedIn Twitter