> For the complete documentation index, see [llms.txt](https://griffin-ai.gitbook.io/knowledge-base/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://griffin-ai.gitbook.io/knowledge-base/ai-agent-framework/core-concepts-and-definitions.md).

# Core concepts and definitions

This section outlines the core concepts that define AI agents in Griffin AI, rooted in foundational principles adapted from widely recognized frameworks in artificial intelligence. \
 \
“**AI Agents**” are digital entities with semi-autonomous capabilities, capable of executing designated tasks or services. Griffin AI develops specialized tools and frameworks tailored for blockchain applications, aimed at augmenting the autonomy of AI Agents within the blockchain ecosystem.  \
 \
To guide AI agent development within Griffin AI, the following core principles according to Amazon[^1] are applied:  

1. Sensory Perception: AI agents must be capable of sensing and perceiving their environment to gather necessary information. 
2. Informed Decision-Making: The agents should use the data obtained from their environment to make informed decisions. 
3. Action-Oriented: Decisions must translate into actions that are executed by the agents in the blockchain environment. 
4. Rationality: Actions undertaken should be rational, aiming to optimize performance and achieve the most favorable outcomes. 

Building on these principles, Griffin AI classifies [AI agents into five distinct categories](#user-content-fn-1)[^1]. Each category reflects a different level of capability and intelligence, catering to specific types of tasks within the blockchain framework: \
 

| Category            | Description                                                                                                                                                                      | Exemplary Task                                                                                                                                  |
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
| **Reflex Agents**   | <p>Operate based on the current situation without consideration for past interactions. Actions are triggered by specific conditions in response to user events. </p><p> </p>     | <p>“Give me my current balance”, “today currency rates” </p><p> </p>                                                                            |
| **Model-based**     | <p>These agents incorporate an internal model of their environment that informs their decisions, allowing for responses that consider both current and past states. </p><p> </p> | ”I want to buy 1 bitcoin at the price x”                                                                                                        |
| **Goal-based**      | <p>Enhance decision-making by incorporating information about desired outcomes, enabling agents to strive towards specific objectives. </p><p> </p>                              | “I want to invest $100 per month over the next six months in Ethereum Layer 2 projects”                                                         |
| **Utility-based**   | <p>Evaluate potential actions based on a utility function, which assesses the likelihood of success and resource requirements to maximize the benefits of actions. </p><p> </p>  | <p>“I want to restake my $ETH with EigenLayer and delegate it to AVSs, with an APR of at least X% and a risk factor of at most Y%” </p><p> </p> |
| **Learning Agents** | <p>Adapt and improve over time by learning from their environment and feedback, enhancing their decision-making and operational efficiency. </p><p> </p>                         | <p>Utilize LLMs to obtain probability estimates for various events before placing bets </p><p> </p>                                             |

Table 8: Categories of Agents&#x20;

&#x20;&#x20;

The concept of "**Tasks**" within Griffin AI is central to the functionality of AI agents. These are specific assignments that outline all the necessary details required for execution, including descriptions of the task, the agents responsible, and the tools they need to employ. &#x20;

&#x20;

Tasks vary in complexity and may involve collaborative efforts among multiple agents, often referred to as an "**Agent Crew**." This collaborative framework allows for the pooling of skills and resources, enhancing the efficacy and scope of the tasks undertaken. Utilizing the AI Agent Registry (see Chapter 8), AI agents are enabled to form their own crews to solve specific tasks. &#x20;

&#x20;

An integral component of agent’s operational capabilities within Griffin AI is the array of “**Tools**” provided to them. These tools are designed to be multifunctional and adaptable to a wide range of needs, supporting activities across various domains. They enable AI agents to perform web searches, conduct thorough data analyses, and engage in complex problem-solving. Unique Griffin AI are the tools provided to agents to interact with blockchain and smart contract functions. This is further explained in Chapter "Blockchain specific toolset and frameworks”.  \
&#x20;\
Beyond individual capabilities, these tools facilitate collaboration among agents, allowing for the delegation of tasks and the sharing of insights and results. This not only empowers AI agents to execute their designated tasks efficiently but also enhances their ability to interact and cooperate with other agents within the network.

[^1]: “What are AI Agents?” (2024), [<mark style="color:yellow;">aws.amazon.com</mark>](https://aws.amazon.com/what-is/ai-agents/)


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## 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, and the optional `goal` query parameter:

```
GET https://griffin-ai.gitbook.io/knowledge-base/ai-agent-framework/core-concepts-and-definitions.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.