Interpreting Fetch.AI: An Intelligent Open Infrastructure Based on Cosmos

Fetch.AI is a technology company that deeply integrates blockchain and artificial intelligence (AI) technologies to build a decentralized intelligent economy by combining AI, blockchain, and Internet of Things (IoT) technologies to achieve distributed goals. The company’s goal is to provide a new way for enterprises and consumers to engage in economic interactions, achieving more efficient, secure, and intelligent transactions.

Thanks to the high intelligence and open architecture of AI+ blockchain, Fetch.AI’s application scenarios are very diverse, including logistics, supply chain, finance, energy, medicine, and many other fields. The technology architecture of Fetch.AI mainly includes two parts: the Fetch.AI main chain and the Fetch.AI intelligent agent. The Fetch.AI main chain is a distributed ledger based on blockchain technology, which is used to record transactions and smart contracts and ensure the security and reliability of transactions. The Fetch.AI intelligent agent is an intelligent contract with AI capabilities, which can autonomously execute tasks, coordinate resources, and interact with other intelligent agents to achieve automated, intelligent, and decentralized economic interactions.

This article will not go into too much detail about the main chain part. Instead, we will focus on the Autonomous Economic Agent (AEA) architecture and the collective learning (Colearn) mechanism to demonstrate how AI participates in the operation and data application process of the blockchain system.

Let the network nodes manage themselves: Autonomous Economic Agent (AEA) architecture

On the network, individuals or companies with data are represented by their agents and connected with agents looking for data. Agents operate on the Open Economic Framework (OEF). This serves as a search and discovery mechanism, where agents representing data sources can announce the data they have access to. Similarly, individuals or companies looking for data can use the OEF to search for agents who have access to relevant data.

Fetch.AI’s AEA architecture is a distributed intelligent agent architecture used to build a self-organizing intelligent agent network. AEA represents Autonomous Economic Agent, whose core idea is to combine artificial intelligence and blockchain technology to build a decentralized intelligent economy, achieving intelligent, autonomous, and decentralized economic interactions.

The core components of the AEA architecture mainly include the following four modules:

  • AEA Agent: The AEA agent is an autonomous and programmable intelligent agent with the ability to make decisions, collaborate autonomously and learn autonomously. It is the core component of the AEA and represents an independent entity with the ability to make decisions and take action. Each AEA agent has its own wallet address, identity, and smart contract and can interact and cooperate with other agents.

  • AEA Communication: AEA communication is a peer-to-peer communication protocol based on blockchain technology, which is used to realize information transmission and interaction between agents. AEA communication can ensure the security and reliability of interactions. Fetch.AI’s AEA supports multiple connection methods, including WebSocket and HTTP connections.

  • AEA Skill: AEA skill is a plug-in module used to extend the functionality and capabilities of the AEA agent. Each skill includes a smart contract and a Python package used to implement specific functions of the agent, such as natural language processing, machine learning, decision-making, etc. Skills can include multiple protocols and models so that agents can understand and respond to requests from other agents.

  • AEA Protocol: AEA protocol is a collaboration mechanism used to achieve collaboration and interaction between agents. AEA protocol defines the message format, protocol flow, and interaction rules between agents, thereby achieving collaborative work between agents. The protocol is the rules and guidelines for communication between agents. The protocol defines how agents should exchange information, respond to requests, and handle errors. Fetch.AI’s AEA supports multiple protocols, including Fetch.AI’s Agent Communication Language (ACL) and HTTP protocol.

Imagine a company looking for data to train predictive models. When the company’s agent connects to the agent representing the data source, it will ask for information about the terms of trade. Then, the agent representing the data provider’s work will provide the terms on which it is willing to sell data. The agent selling data access may seek as high a price as possible, while the agent buying data access would like to pay as low a price as possible. However, the data-selling agent knows that if it charges too high a price, it will miss out on the deal. This is because the agent seeking data will not accept these terms, but will try to buy the data from another source on the network. If the purchasing agent does find the terms acceptable, it will pay the agreed-upon price to the sales agent via a transaction on the ledger. Once payment is received, the agent selling the data will send encrypted data over the network.

Aside from the initial setup, the entire process is fully automated and executed by agents. This means that company employees can continue to work without disruption and the predictive model can accumulate relevant, anonymous data. By obtaining the data, the company purchasing the information can more effectively train its model, which can then be used for more accurate predictions. Such predictions can be used in any industry.

The core of intelligent nodes: AEA skill modules and collective learning (Colearn) mechanisms

Of the four modules above, the most important is the AEA skill module, which is the key module that allows nodes to have intelligence. AEA skills are a plug-in module for implementing the autonomous learning function of agents. Each learning skill includes an intelligent contract and a Python package for implementing different types of learning tasks, such as reinforcement learning, supervised learning, unsupervised learning, etc. When an agent needs to learn, it can choose the appropriate learning skill and save the learning result in its own state. Agents can autonomously adjust behavior and strategy based on learning results, resulting in smarter, more efficient, and more sustainable economic interactions.

The collective learning principle of Fetch.AI includes the following steps:

  • Data sharing: Different agents collect their own data and upload it to a shared database on the blockchain network. This data can be sensor data, text data, image data, etc. All agents participating in collective learning can access the data in the shared database and use it for training.

  • Model training: Agents use data from the shared database for model training. The model can be a machine learning model, a deep learning model, or other types of algorithms. Agents can use different models for training in order to learn different tasks or problems.

  • Model selection: After model training is complete, agents upload their models to the blockchain network. All agents participating in collective learning can access these models and choose the one that best suits their needs based on their performance, task requirements, resource constraints, and other factors.

  • Model integration: After selecting the model, agents can integrate it with their own skills to better perform their tasks. Skills can be modules that process specific types of tasks, such as cryptocurrency transactions, logistics management, etc. Agents can use multiple skills and models to process tasks.

  • Reward mechanism: During collective learning, agents can receive rewards for contributing their data and models. Rewards can be allocated based on agent performance, contribution, resource utilization efficiency, and other factors. The reward mechanism can encourage agents to actively participate in collective learning and improve the performance of the entire system.

Assume there are two agents, A and B, who need to cooperate to complete a task, such as transporting goods. Agent A is responsible for providing the goods, and Agent B is responsible for providing transportation services. In the initial interaction, both Agent A and Agent B can adopt random behavioral strategies to complete the task, such as randomly selecting transportation routes or modes.

As the interaction progresses, Agent A and Agent B can learn the interaction history data through learning skills, and autonomously adjust their behavioral strategies based on the learning results. For example, Agent A can learn about information such as the supply of goods and transportation costs through learning skills, and autonomously select the optimal cooperation strategy based on the current demand for goods and market prices. Agent B can also learn about the efficiency and cost of transportation routes and modes through learning skills, and autonomously select the optimal transportation strategy based on the current traffic conditions and energy prices.

As the interaction continues and the learning results are continuously updated, Agent A and Agent B can gradually optimize their behavioral strategies, thereby achieving more efficient, intelligent, and sustainable economic interactions. This autonomous learning process can be continuously iterated and optimized, thereby achieving better economic benefits and social value.

It should be noted that autonomous learning functionality requires agents to have sufficient computing power and data resources to achieve good learning results. Therefore, in practical applications, appropriate learning skills and resource configurations should be selected based on the actual situation and needs of the agents, thereby achieving the best learning results.’s core autonomous economic agent (AEA) has achieved the goal of intelligent, autonomous, and decentralized economic interactions. Its advantages lie in the deep integration of artificial intelligence and blockchain technology, as well as the design of autonomous economic agents. These AEA agents can autonomously learn, make decisions, and freely interact in a decentralized environment, thereby improving the efficiency and intelligence of economic interactions. In addition, Fetch.AI’s co-learning mechanism encourages agents to actively participate and improve the performance of the entire system by sharing data and models.

However, Fetch.AI also faces some challenges. Firstly, its autonomous learning functionality requires a relatively high demand for computing power and data resources, which may limit its application in resource-limited environments. Secondly, Fetch.AI’s technical architecture and functionality are relatively complex, requiring higher technical thresholds and learning costs, which may have an impact on its widespread application.


Looking forward, the future of Fetch.AI is still promising. With the continuous development of technology, it may introduce more AI and blockchain technologies to enhance performance and efficiency, and meet more application scenarios and demands. At the same time, as privacy protection and data security are increasingly valued, Fetch.AI’s decentralization and security features may receive more attention and application. Despite some challenges, Fetch.AI’s innovation and potential in the fields of AI and blockchain are still worth exploring and paying attention to.


[1]Fetch.AI Developer Documentation

[2]Melanie Mitchell: AI 3.0

[3]Alexey Potapov: Basic Atomese Features required

Disclaimer: This article is for research reference only and does not constitute any investment advice or recommendation. The project mechanism introduced in this article represents only the author’s personal opinions and has no relationship with the author or this platform. There are many uncertainties such as high market risks, policy risks, and technical risks in blockchain and digital currency investment. The token prices in the secondary market fluctuate sharply, and investors should make decisions cautiously and independently bear investment risks. The author of this article or this platform is not responsible for any losses incurred by investors due to the use of the information provided in this article.

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