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Demystifying AI Agents: Understanding the Building Blocks of Artificial Intelligence

Demystifying AI Agents: Understanding the Building Blocks of Artificial Intelligence

At the heart of AI’s capabilities are intelligent systems known as AI agents. These agents possess the ability to perceive their environment, make decisions, and take actions based on their objectives. In this blog post, we will demystify AI agents by exploring their fundamental building blocks and shedding light on how they operate.

An Introduction to Multi-Agent Systems

An Introduction to Multi-Agent Systems

A multi-agent system (MAS) is a computational framework consisting of multiple interacting agents or autonomous entities, each with its own goals, knowledge, and decision-making capabilities. These agents can be software programs, robots, or any other smart entities that can perceive their environment, reason, learn, communicate, and take actions to achieve their objectives.

ChatGPT Fine Tuning

ChatGPT Fine Tuning

ChatGPT fine-tuning refers to the process of refining a pretrained language model, such as OpenAI’s GPT (Generative Pre-trained Transformer), for a specific chatbot or conversational AI application. Fine-tuning involves training the model further on a narrower dataset, which is curated or generated based on the intended purpose and domain of the chatbot.

AI Agents

AI Agents

AI agents are autonomous or semi-autonomous software programs that utilize artificial intelligence techniques to perform various tasks, make decisions, and interact with their environment or other agents.

Autonomous AI Agents

Autonomous AI Agents

Both OODA and PDCA are used in autonomous AI decision-making. OODA is used to make decisions in dynamic environments, while PDCA is used to make decisions in stable environments. In autonomous AI decision-making, the OODA model is used to observe the environment, orient the AI system to the situation, decide on a course of action, and act on the decision.

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