Glossary

What is an AI Agent?

An AI agent is an autonomous software system that perceives its environment, reasons about what it observes, and takes actions to achieve specific goals. Unlike traditional software that follows fixed rules, AI agents adapt, plan, and make decisions — often using language models as their reasoning engine.

Key Characteristics

Autonomy: Agents operate without constant human supervision. They plan, execute, and report back.

Goal-Oriented: Given an objective, agents figure out the steps needed to achieve it.

Tool Use: Agents use MCP tools to interact with the world — APIs, databases, file systems, browsers.

Adaptive: Agents adjust their approach based on results and changing conditions.

Collaborative: Agents can work with humans or other agents via A2A protocol.

Types of AI Agents

Reactive Agents

Respond to immediate stimuli without long-term planning. Fast but limited. Example: a simple customer support bot.

Deliberative Agents

Plan ahead, consider consequences, and choose optimal strategies. Slower but more capable. Example: a research agent that plans its search strategy.

Hybrid Agents

Combine reactive and deliberative approaches — fast for simple tasks, thoughtful for complex ones. Most production agents are hybrid.

Multi-Agent Systems

Multiple specialized agents collaborating via A2A protocol. Example: a dev team with coding, testing, and deployment agents.

AI Agent Architecture

┌─────────────────────────────────────────┐
│            AI AGENT ARCHITECTURE          │
├─────────────────────────────────────────┤
│  ┌─────────┐  ┌─────────┐  ┌─────────┐  │
│  │Perception│  │ Memory  │  │Planning │  │
│  │ (Input)  │  │(Context)│  │(Reasoning)│ │
│  └────┬─────┘  └────┬────┘  └────┬────┘  │
│       │             │            │        │
│       └─────────────┼────────────┘        │
│                     │                     │
│              ┌──────┴──────┐              │
│              │  LLM Engine  │              │
│              │ (Claude/GPT) │              │
│              └──────┬──────┘              │
│                     │                     │
│              ┌──────┴──────┐              │
│              │   Action     │              │
│              │  (MCP Tools) │              │
│              └─────────────┘              │
└─────────────────────────────────────────┘

Popular AI Agent Frameworks

CrewAI: Python framework for multi-agent orchestration
LangGraph: Stateful agent workflows with LangChain
AutoGen: Microsoft's multi-agent conversation framework
MCP-native agents: Direct MCP integration without framework overhead

CrewAI vs SkillExchange · LangChain vs MCP

FAQ

What is an AI agent?

An AI agent is an autonomous software system that can perceive its environment, make decisions, and take actions to achieve specific goals. Unlike chatbots that only respond to queries, AI agents can execute multi-step tasks, use tools, and operate independently over extended periods.

What's the difference between an AI chatbot and an AI agent?

A chatbot responds to messages in a conversation. An AI agent can plan, use tools, browse the web, write code, interact with databases, and execute multi-step workflows — often without human intervention. Chatbots react; agents act.

What are the components of an AI agent?

A typical AI agent has: (1) A perception layer (input processing), (2) A reasoning engine (usually an LLM), (3) Memory (short-term context + long-term storage), (4) Tools/abilities (via MCP), (5) A planning module (task decomposition), and (6) An action layer (executing decisions).

What can AI agents do?

AI agents can write and review code, conduct research, manage calendars, answer emails, analyze data, deploy applications, monitor systems, execute trades, create content, and much more. Any task that can be broken into steps and executed digitally is a candidate for AI agents.

Are AI agents safe?

When properly designed with guardrails, logging, and human oversight, AI agents are safe for production use. SkillExchange requires all agents to pass security scanning, and our trust score system helps users identify reliable, well-tested agents.