GOAT.AI - Task to AI Agents 2.2.6

The Goal-oriented Orchestration of Agent Tasks is a sophisticated system that facilitates seamless communication and collaboration among AI agents to execute specific tasks. Instead of relying on a single AI agent, this system leverages the expertise of multiple agents to achieve optimal results.

For instance, let's consider the example of selecting the best day for a 20km semi-marathon next month. The Weather agent retrieves accurate forecasts, the Web search agent identifies optimal running conditions, and the Wolfram agent calculates the "best day." This interconnectedness of AI agents streamlines complex tasks with ease and precision.

The concept of LLMs as the central mainframe for autonomous agents is intriguing. Demonstrations like AutoGPT, GPT-Engineer, and BabyAGI provide simple illustrations of this idea. LLMs have the potential to go beyond generating well-written copies, stories, essays, and programs. They can serve as powerful General Task Solvers, which is precisely what the Goal-oriented Orchestration of Agent Taskforce (GOAT.AI) aims to achieve.

To ensure the efficient functioning of the goal-oriented orchestration system, three core components must operate effectively:

1. Planning
The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex assignments. The agent engages in self-critique and reflection on past actions, learning from mistakes and refining approaches for future steps, resulting in improved overall outcomes.
2. Memory
Short-term memory refers to the model's ability to process a significant amount of text without any degradation in quality. Currently, LLMs can provide high-quality answers for approximately 128k tokens. Long-term memory allows the agent to store and recall unlimited amounts of information over extended periods. This is achieved by utilizing an external vector store for efficient RAG systems.
3. Action Space
The agent gains the capability to call external APIs, acquiring additional information beyond its pre-training. This includes accessing real-time information, executing code, accessing proprietary information sources, and invoking other agents for information retrieval. The action space also encompasses performing specific actions to obtain desired outcomes, such as sending emails, launching apps, or opening doors. These actions are typically executed through various APIs. Furthermore, agents can invoke other agents for actionable events within their domain of access.

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