GOAT.AI - Task to AI Agents Description
Goal-oriented orchestration of Agent Tasks. Basically, AI Agents will communicate to each other to execute your task.
Example: "pick the best day next month for a 20km semi-marathon". AI will start collaborating: the Weather agent retrieves forecasts, the Web search agent identifies optimal running conditions, and the Wolfram agent calculates the "best day." It's the art of connected AI, simplifying complex tasks with sophistication.
LLMs as the central mainframe for autonomous agents is an intriguing concept. Demonstrations like AutoGPT, GPT-Engineer, and BabyAGI serve as simple illustrations of this idea. The potential of LLMs extends beyond generating or completing well-written copies, stories, essays and programs; they can be framed as powerful General Task Solvers, and that is what we aim to achieve in building the Goal Oriented Orchestration of Agent Taskforce (GOAT.AI)
For a goal-oriented orchestration of an LLM agent task force system to exist and function properly, three main core components of the system have to function properly
- Overview
1) Planning
- Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, making it easier to handle complex assignments efficiently.
- Reflection and refinement: The agent engages in self-critique and self-reflection on past actions, learns from mistakes, and improves approaches for future steps, thereby enhancing the overall quality of outcomes.
2) Memory
- Short-term memory: It refers to the amount of text the model can process before answering without any degradation in quality. In the current state, the LLMs can provide answers without any decrease in quality for approximately 128k tokens.
- Long-term memory: This enables the agent to store and recall an unlimited amount of information for the context over long periods. It is often achieved by using an external vector store for efficient RAG systems.
3) Action Space
- The agent acquires the ability to call external APIs to obtain additional information that is not available in the model weights (which are often difficult to modify after pre-training). This includes accessing current information, executing code, accessing proprietary information sources, and most importantly: invoking other agents for information retrieval.
- The action space also encompasses actions that are not aimed at retrieving something, but rather involve performing specific actions and obtaining the resulting outcome. Examples of such actions include sending emails, launching apps, opening front doors, and more. These actions are typically performed through various APIs. Additionally, it is important to note that agents can also invoke other agents for actionable events that they have access to.
Example: "pick the best day next month for a 20km semi-marathon". AI will start collaborating: the Weather agent retrieves forecasts, the Web search agent identifies optimal running conditions, and the Wolfram agent calculates the "best day." It's the art of connected AI, simplifying complex tasks with sophistication.
LLMs as the central mainframe for autonomous agents is an intriguing concept. Demonstrations like AutoGPT, GPT-Engineer, and BabyAGI serve as simple illustrations of this idea. The potential of LLMs extends beyond generating or completing well-written copies, stories, essays and programs; they can be framed as powerful General Task Solvers, and that is what we aim to achieve in building the Goal Oriented Orchestration of Agent Taskforce (GOAT.AI)
For a goal-oriented orchestration of an LLM agent task force system to exist and function properly, three main core components of the system have to function properly
- Overview
1) Planning
- Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, making it easier to handle complex assignments efficiently.
- Reflection and refinement: The agent engages in self-critique and self-reflection on past actions, learns from mistakes, and improves approaches for future steps, thereby enhancing the overall quality of outcomes.
2) Memory
- Short-term memory: It refers to the amount of text the model can process before answering without any degradation in quality. In the current state, the LLMs can provide answers without any decrease in quality for approximately 128k tokens.
- Long-term memory: This enables the agent to store and recall an unlimited amount of information for the context over long periods. It is often achieved by using an external vector store for efficient RAG systems.
3) Action Space
- The agent acquires the ability to call external APIs to obtain additional information that is not available in the model weights (which are often difficult to modify after pre-training). This includes accessing current information, executing code, accessing proprietary information sources, and most importantly: invoking other agents for information retrieval.
- The action space also encompasses actions that are not aimed at retrieving something, but rather involve performing specific actions and obtaining the resulting outcome. Examples of such actions include sending emails, launching apps, opening front doors, and more. These actions are typically performed through various APIs. Additionally, it is important to note that agents can also invoke other agents for actionable events that they have access to.
Open up
