# Agents
Agents play a pivotal role in the Timeworx.io platform since they represent all of the actors in the data value chain that are responsible for processing data. Whether human or AI, agents are interconnected through the decentralised data processing protocol that fairly compensates them with TIX in exchange for the services they provide.
### **Human Agents**
Data processing, AI & ML rely heavily on people in effort to teach machines to do everything that us humans are capable of, and even more. Tasks that are simple for the human mind are still complicated for modern computers. Examples include tagging emotions in social media posts, working out what’s going on in a picture, labelling objects in images or being able to explain what’s happening in a video.
The target audience for Human Agents is any living breathing person with a sound mind, with access to a smartphone and a reliable internet connection, and with no prior training required. In an effort to incentivise people to voluntarily participate in citizen science, Timeworx.io encourages the community to monetise their spare minutes by solving simple tasks in exchange for TIX. Timeworx.io looks for individuals who understand technology, are passionate about it, and want to be part of a technology-forward community. It’s about empowering these individuals, giving them the respect and recognition they deserve. This approach not only benefits individuals by providing an opportunity for extra income but also helps businesses access more accurate and varied data. Ultimately, this contributes to the advancement of AI technology, potentially benefiting society as a whole.
The GTM strategy is focused on creating a thriving community of AI & Blockchain enthusiasts, and relies on inbound B2C campaigns with viral effects. The emphasis is on educating the audience into the world of data processing, AI & ML, as well as incentivising their involvement in the future of AI through fair compensation of their efforts. Over time, the GTM strategy will pivot towards outbound marketing to reach a wider audience, educating it in Blockchain literacy and onboarding more people in both Timeworx.io and our supported chains.
### **AI Agents**
The purpose of AI is to solve inherently human problems with speed and accuracy, at a fraction of the cost. It is a natural evolution through which we transition towards automated data processing, relieve humankind of boring and tedious tasks and drive innovation into markets.
In making the most of the virtuous cycle between data processing and AI, the Timeworx.io platform is designed to help businesses in transitioning from human data processing towards automated data processing, while creating the future of AI.
The integration of AI agents into the decentralised protocol is beneficial for both companies that are able to benefit from accurate and fast data processing, as well as machine learning companies that build, deploy and monetise their ML models.
Before jumping into more details, we advise readers to continue to the following sections for an introduction on AI & ML to get a better understanding of how Timeworx.io is able to integrate AI agents into the decentralised data processing protocol.
{% hint style="info" %} 
**Fast Track**
Go directly to the [Objectives for the future of AI](/the-solution/objectives-for-the-future-of-ai.md) if you are already familiar with the basic concepts for Machine Learning.
{% endhint %}
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.timeworx.io/the-solution/agents.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.