The architecture of Agent agent can be clearly divided into several key parts, and the following is a detailed summary of its architecture: First, perception and interaction layer perception ability: the agent obtains environmental information through sensors and other devices.

Sensors similar to robots help it obtain information about the surrounding environment without affecting the environment.

This is the basis for the agent to interact with the external environment.

Interactive capabilities: Agents are able to interact with the environment, including receiving and parsing input from the environment, and output actions or decisions to the environment.

Task planning: This layer is responsible for breaking down complex goals into executable sub-tasks and dynamically adjusting execution strategies.

It enables the agent to respond flexibly to uncertainty and demonstrate a human-like approach to problem solving.

Decision making: Based on perceived information and task objectives, the intelligent body makes decisions at this level and chooses the best course of action.

Third, learning and memory level learning ability: the agent has the learning ability, can establish the cognition of the world through self-supervised learning and other ways, and constantly optimize their own decision-making and action strategies.

Memory management: The agent has a memory management module that allows it to maintain context and form its own "cognition" over multiple rounds of conversations.

This ability allows the agent to maintain state during long periods of task execution and record historical interaction information.

Execution and control layer Action execution: According to the output of the decision layer, the executive layer is responsible for transforming the decision into a specific action or instruction, and controlling the behavior of the agent to achieve the task goal.

Real-time monitoring and feedback: The executive level is also responsible for monitoring the agent's behavior and environmental feedback in real time in order to adjust the action strategy or provide new information to the decision level.

Hardware platform: The hardware platform of the agent provides basic resources such as computing, storage and communication to ensure the normal operation of the agent.

Software framework: Software framework provides support for the development, deployment and operation of agents, including data processing, model training, inference acceleration and other functions.

To sum up, the architecture of Agent agent includes perception and interaction layer, decision and planning layer, learning and memory layer, execution and control layer and foundation support layer.

These levels work together to enable the agent to perceive the environment, make decisions, perform tasks, and constantly learn and optimize its own behavior.