The architecture of an AI agent depends on the specific task or domain. For simple tasks, rule-based or decision tree architectures work well. For more complex problems, deep learning architectures such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs) may be required. In reinforcement learning, the architecture often consists of a policy network, a value network, or both in an Actor-Critic setup. For multi-agent systems, a decentralized or centralized architecture can be adopted depending on how the agents communicate with each other. Ultimately, the best architecture is one that aligns with your specific goals, computational constraints, and the complexity of the problem you are trying to solve.
Source: https://www.inoru.com/ai-agent-development-company
