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Agent -based mining is an interdisciplinary field that combines multi-agent systems with data mining and machine learning to solve business problems and solve problems in science.

Agents can be described as decentralized computing systems that have both computing and communication capabilities. Agents are modeled based on data processing and information gathering algorithms such as «agent problem» which is a machine learning technique that tries to find solutions to business problems without any data center.

Agents are like distributed computers where users share computing resources with each other. This allows agents to exchange payloads and process data in parallel, effectively speeding up processing and allowing agents to complete their tasks faster.

A common use of agents is data processing and communication, such as the task of searching and analyzing large amounts of data from multiple sources for specific patterns. Agents are especially efficient because they don’t have a centralized server to keep track of their activities.

Currently, there are two technologies in this area that provide the same functionality as agents, but only one of them is widely used: distributed computing, which is CPU-based and often uses centralized servers to store information; and local computing, which is typically based on local devices such as a laptop or mobile phone, with users sharing information with each other.