Define the Action Layer
Last updated
Last updated
The Action Layer refers to a component within an AI agent system that is responsible for translating the decisions of the agent system into actual actions and interacting with the external environment. In the Action Layer, the agent system achieves specific goals or tasks by executing a series of actions. These actions may involve controlling physical devices, manipulating software interfaces, sending commands over networks, or processing other forms of input and output. The design of the Action Layer needs to consider the goals of the agent system, the complexity of the environment, and the efficiency and reliability of action execution. Typically, the Action Layer includes functions such as action selection, execution, and monitoring to ensure that the agent system can effectively interact with the external world and achieve the intended outcomes.
The Action Layer is the most critical component of an AI Agent's journey towards value realization. It involves translating the model's deliberations into effective actions, interacting with the real world, and delivering end-to-end value. Due to the complexity of the real world, establishing the Action Layer is also the most complex and long-tailed process. As the pioneers and builders of the first Action Layer, we can broadly categorize the real world into five abstract application domains: Consumer Apps, Application Programming, Blockchain, Roads, and the Physical World.
On the other side of the Action Layer is the framework of the Agent, which comprises a series of functional modules. Common modules include Observation, Planning, Reward & Evaluation, and Memory, each supported by various specific technical means, such as models, rules, or algorithms.
As the founders of IntentAGI, we have pioneered a framework specifically designed for WebAgents. Within this framework, we support end-to-end usability from user intent to web operation actions, achieving a 5x improvement in accuracy and a 90% reduction in inference costs compared to GPT-4 for similar tasks. This allows developers using our framework to more easily develop agents with practical value. In fact, with just one complete set of training data, we can achieve over 95% accuracy in one-shot mode, approaching 100% accuracy with multiple enhancements. This enables everyone to become developers of AI agents. Additionally, we welcome more contributors to explore further possibilities in providing frameworks to enhance efficiency and the boundaries of AI agents. We will also provide reasonable benchmarks based on different framework scenarios to promote the effective enhancement of AI agent frameworks.
