Understanding Agents In Generative AI

AI agents are applications designed to observe, reason, and act upon the world autonomously.

They combine reasoning capabilities, external tool usage, and planning to execute complex tasks, unlike standalone language models, which are limited to generating content based on training data.

▶️ Key Components of AI Agents

◾ The Model: The core decision-making component, often a language model (LM) like GPT or Gemini, which processes inputs and generates outputs.

◾Tools: Interfaces for interacting with external systems or data, such as APIs or databases, enabling the agent to access real-world information and perform actions.

◾The Orchestration Layer: Governs the agent's decision-making cycle, ensuring logical reasoning, memory management, and continuous goal-oriented behavior.

▶️Key Distinctions: Agents vs. Models

◾Models generate predictions based on training data; agents extend this by accessing and interacting with external systems.

◾Agents maintain session history and employ cognitive architectures for multi-turn reasoning.

Author

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