Types of AI Agents
Coding Agents
Write, review, test, and deploy code autonomously.
Research Agents
Gather, analyze, and synthesize information from multiple sources.
Automation Agents
Execute repetitive tasks, manage workflows, and orchestrate pipelines.
Multi-Agent Systems
Coordinate multiple specialized agents for complex objectives.
How AI Agents Work
1. Perception & Input
Agents receive input via text, voice, structured data, or sensor readings. MCP enables agents to read from files, databases, APIs, and other tools.
2. Reasoning & Planning
Using LLMs (Large Language Models), agents analyze input, break down complex tasks into steps, and decide which tools to use.
3. Tool Use & Action
Agents execute actions via MCP tools — calling APIs, writing code, querying databases, or communicating with other agents via A2A protocol.
4. Learning & Feedback
Agents incorporate results, adjust strategies, and improve over time. Production agents log everything for audit trails and continuous improvement.
Frequently Asked Questions
What is an AI agent?
An AI agent is an autonomous software system that perceives its environment, makes decisions, and takes actions to achieve specific goals. Unlike chatbots that only respond to queries, AI agents can execute tasks, use tools, browse the web, write code, and coordinate with other agents.
What are the types of AI agents?
AI agents fall into several categories: reactive agents (respond to stimuli), deliberative agents (plan ahead), hybrid agents (combine both), and multi-agent systems (multiple agents collaborating). In practice, modern agents are often categorized by use case: coding agents, research agents, customer support agents, automation agents, and creative agents.
How do AI agents use tools?
AI agents use tools through protocols like MCP (Model Context Protocol) and function calling. The agent sends a structured request to the tool, receives a response, and incorporates that data into its reasoning. This enables agents to read files, query databases, call APIs, and interact with the physical world.
What is multi-agent orchestration?
Multi-agent orchestration is the coordination of multiple AI agents to solve complex problems. Using protocols like A2A (Agent-to-Agent), agents can delegate tasks, share information, and collaborate on workflows. Frameworks like CrewAI, LangGraph, and AutoGen enable multi-agent systems.
How do I deploy AI agents in production?
Production AI agents need robust error handling, logging, monitoring, and security scanning. SkillExchange provides the infrastructure for deploying agents with trust scores, usage analytics, automated retry logic, and MCP-native tool integration. Start with a single agent and scale to multi-agent systems.