What Is an AI Agent? Definition, Core Capabilities, and Architecture

What is an AI Agent

An AI Agent (intelligent agent) perceives its environment, formulates a plan, uses tools, and iteratively executes a "think → act → observe → think" loop until a goal is achieved. It extends a large language model with a continuous execution loop that ties together memory, tool use, planning, and reflection.

Typical Agent Workflow

Automatically search for the latest news, product updates, and financing events of a competitor.

Filter the information and preliminarily verify source reliability.

Structure the findings according to a report template.

Call collaboration‑tool APIs to send the report to Feishu or email.

Summarize the result, e.g., "Report sent with 12 key updates from the past 30 days."

Core Capabilities

1. Planning

Planning decomposes a complex task into executable subtasks. Example: implementing a user‑registration feature may be broken into designing a database schema, writing backend APIs, adding input validation, creating unit tests, and updating API documentation. The planning process relies on techniques such as Chain‑of‑Thought (step‑by‑step reasoning), ReAct (Reasoning + Acting), and explicit task decomposition.

2. Memory

Agents maintain two memory types:

Short‑term memory : the current conversation context, limited by the model’s context window. It records what has been done, obtained results, and remaining steps within a single session.

Long‑term memory : persistent information across sessions, typically stored in vector databases or file systems (e.g., user preferences, architectural conventions, accumulated experience).

3. Tool Use

Tool use expands an agent’s capabilities. Common categories include:

Code execution : Python interpreter for running code and processing data.

Information retrieval : search engines or Retrieval‑Augmented Generation to obtain real‑time or private knowledge.

API calls : sending emails, operating databases, invoking third‑party services.

File operations : reading and writing files directly.

Browser interaction : opening web pages, taking screenshots, manipulating web UI.

Technical implementations have evolved from Function Call to Tool Calling 1.0 and Tool Calling 2.0.

4. Reflection & Error Correction

After each step the agent checks whether the outcome matches expectations, analyzes error messages, retries, or backtracks to a previous state to choose a different path. Upon successful completion the agent performs a self‑review, recording failure lessons for future tasks.

Comparison with Related Concepts

Chatbot : one‑turn or simple multi‑turn text exchange, rarely uses tools, limited to answering.

Agent : receives a goal, autonomously plans and executes multi‑step tasks, and can perform real actions using tools.

Workflow : a fixed, pre‑defined sequence of steps; agents provide dynamic decision‑making within such pipelines.

An "Agentic Workflow" blends both: deterministic steps are orchestrated by a workflow engine, while uncertain nodes are handled by an agent that makes dynamic decisions.

Typical Architecture Patterns

Single‑Agent Architecture : one large model acts as the brain, coupled with a set of tools and a memory module. Suitable for well‑bounded single‑domain tasks such as customer‑service bots.

Multi‑Agent Architecture : multiple agents collaborate when a single agent cannot handle the complexity. Collaboration modes include:

Division of labor (e.g., separate frontend, backend, and testing agents).

Debate/review (execution agent vs. audit agent).

Hierarchical command (manager agent assigns tasks to worker agents).

Representative frameworks: AutoGen, CrewAI, LangGraph, Anthropic’s Agent SDK, OpenAI’s Agents API.