AI Agents

AI Agents represent a paradigm shift from passive question-answering systems to active, goal-oriented autonomous systems. While a standard LLM predicts the next token based on a static prompt, an AI Agent uses the LLM as a “brain” to reason, plan, and execute multi-step workflows to achieve a high-level objective.

Architecture Overview

A robust AI Agent architecture typically consists of four main modules:

1. Profiling: The agent’s identity, role, and persona.
2. Memory:
   • Short-term: Context window limitations, managing the immediate conversation history.
   • Long-term: Storing and retrieving information via Vector Databases (RAG) to maintain state across sessions.
3. Planning: Use of techniques like Chain of Thought (CoT), ReAct (Reason + Act), or Tree of Thoughts to break down complex goals into manageable sub-tasks.
   • Reflection: The ability to critique its own plans and adjust if actions fail.
4. Action: The interface with the external world. This is where the agent utilizes Agent Skills (tools/function calling) to manipulate APIs, browse the web, or query databases.

Technical View

The following diagram illustrates the control flow within an autonomous agent system.

The Role of Skills

The “Action” component is critical. An agent without tools is hallucination-prone and isolated. By defining granular Agent Skills, we provide the deterministic code blocks that the probabilistic LLM can invoke. This separation of concerns allows the LLM to handle the logic of “what to do” while standard software engineering handles the implementation of “how to do it.”