Comprehensive Overview of AI Agents: Concepts, Technical Frameworks, and Applications

OpenAI's recent developer conference introduced the Assistants API, which enables developers to create AI agents (agents) without writing code. An AI agent is a software entity capable of autonomous reasoning, planning, and interaction with its environment, a concept originally proposed by Minsky in 1986.

Large language models (LLMs) such as GPT‑4 and PaLM2 provide the core intelligence for modern agents. Their massive training data and emergent abilities (context learning, reasoning, chain‑of‑thought) allow agents to decompose complex problems, understand multimodal inputs, and generate natural‑language responses.

The technical architecture of an LLM‑based agent consists of three modules: Perception (multimodal sensing of text, images, audio, and other signals), Brain (the LLM that stores knowledge, performs reasoning, planning, and memory management), and Action (text output, tool usage, API calls, or embodied actions in physical/virtual environments).

Retrieval‑Augmented Generation (RAG) is used to overcome the limited context window of LLMs by retrieving relevant documents from a vector database and feeding them into the prompt. Effective RAG requires careful document chunking, query formulation, and result ranking.

Task planning in agents relies on prompting techniques such as Chain‑of‑Thought (CoT) and Tree‑of‑Thought (ToT) to break a high‑level goal into sub‑tasks, generate execution plans, and reflect on the plan’s quality. Tools and APIs (e.g., search engines, weather services) are invoked when the LLM decides they are needed.

Embodied agents extend the framework to physical robots or virtual worlds, adding observation, manipulation, and navigation capabilities. Recent models like PaLM‑E and VoxPoser demonstrate end‑to‑end learning of language‑conditioned motor commands.

Application scenarios are divided into single‑agent and multi‑agent settings. Single‑agent agents (task‑oriented, innovative, lifecycle‑oriented) include AutoGPT, ChatGPT+ with code interpreter, and LangChain agents. Multi‑agent systems coordinate several specialized agents (researcher, editor, writer, reviewer) through cooperative (ordered or unordered) or adversarial interactions.

Microsoft's open‑source AutoGen framework provides a generic infrastructure for building multi‑agent applications. It defines a ConversableAgent base class and three concrete agents: AssistantAgent (LLM‑driven code generation), UserProxyAgent (human proxy that can execute code), and GroupChatManager (dialogue manager). AutoGen supports both static (ordered) and dynamic (unordered) conversation flows, enabling dialogue‑driven programming.

The article concludes with a discussion of current limitations of AI agents: limited controllability, memory and token constraints, difficulty handling parallel tasks, low reliability for long‑running processes, and the reliance on generic LLM capabilities without specialized fine‑tuning.