Reinforcement Learning from Human Feedback (RLHF) for AI Agents - Part 6

Introduction to Reinforcement Learning from Human Feedback (RLHF) for AI Agents - Part 6

Agentic AI represents a paradigm shift from conversational AI to autonomous systems that can plan, reason, and execute tasks on behalf of users. In this post, we explore the nuances of this technology.

An AI agent typically consists of a large language model (LLM) as its 'brain', equipped with various tools (like web search, code execution, or database access), and a memory system to maintain context across interactions.

The Core Components

• Planning: The ability to break down complex tasks into subtasks.
• Memory: Short-term and long-term context retention.
• Tool Use: Interacting with external APIs and environments.

As we continue to develop these systems, the line between software tools and artificial collaborators will blur, ushering in a new era of productivity.