An AEA is an intelligent agent whose goal is generating economic value for its owner. It can represent machines, humans, or data.
There are at least five general application areas for AEAs:
- Inhabitants: agents paired with real world hardware devices such as drones, laptops, heat sensors, etc. An example is the theremometer agent that can be found here.
- Interfaces: facilitation agents which provide the necessary API interfaces for interaction between old (Web 2.0) and new (Web 3.0) economic models. An example is the http skill in this agent.
- Pure software: software agents living in the digital space that interact with inhabitant and interface agents and others.
- Digital data sales agents: pure software agents that attach to data sources and sell it via the open economic framework. An example can be found here.
- Representative: an agent which represents an individual's activities on the Fetch.ai network. An example can be found here.
Likely short-term applications
In the short-term we see AEAs primarily deployed in three areas:
Off-load repetitive tasks: AEAs can automate well defined processes in supply chain, transport and finance.
Micro transactions: AEAs make it economically viable to execute trades which reference only small values. This is particularly relevant in areas where there is a (data) supply side constituted of many small actors and a single demand side.
Wallet agents: AEAs can simplify the interactions with blockchains for end users. For instance, they can act as "smart wallets" which optimize blockchain interactions on behalf of the user.
Multi-agent system versus agent-based modelling
The Fetch.ai multi-agent system is a real world multi-agent technological system and, although there is some overlap, it is not the same as agent based modelling where the goal is scientific behavioural observation rather than practical economic gain.
Moreover, there is no restriction to multi. Single-agent applications are also possible.