AI Agents
AI that takes actions, not just answers — the future of automation
Contents
What is an AI Agent?
A chatbot answers questions. An AI Agent takes actions.
An agent is an LLM with the ability to use tools — it can browse the web, write and run code, send emails, interact with APIs, and even control your computer. You give it a goal, and it figures out the steps to achieve it.
Think of it as the difference between asking a consultant for advice (chatbot) versus hiring an employee who actually does the work (agent).
How agents work
Agents use a loop called ReAct (Reason + Act):
1. Reason: Given the goal, what is the next step? 2. Act: Use a tool (search, code execution, API call) 3. Observe: See the result of the action 4. Repeat: Until the goal is achieved
Example: "Research the top 5 AI tools launched this week and write a summary." Agent thinks → uses web search → reads results → thinks → synthesises → writes summary → done.
Types of agents
Single agents: One LLM with multiple tools. Good for focused tasks.
Multi-agent systems: Multiple specialised agents working together. One agent researches, another writes, another reviews — like a virtual team.
Computer use agents: Control actual desktop applications. Claude Computer Use, OpenAI Operator, and Google Project Mariner can click, type, and navigate websites on your behalf.
Autonomous agents: Run without human approval at each step. Higher risk but much faster.
Real examples of agents today
Cursor (Coding): Reads your codebase, writes code, runs tests, fixes errors — all in a loop until the feature works.
Perplexity Deep Research: Runs 20+ searches, reads full pages, cross-references sources, writes a report — automatically.
OpenAI Operator: Books restaurants, fills forms, completes online tasks for you.
n8n AI Agent node: Chains tools in automated workflows — fetches data, processes with AI, sends to output.
MCP — Model Context Protocol
MCP is Anthropic's open standard for connecting AI agents to external tools and data sources. It's like USB-C for AI — a universal connector.
With MCP, you can give Claude access to your file system, databases, APIs, or any external service through a standardised interface. Tools built for MCP work with any compatible AI model.
For developers, MCP is becoming the standard way to build AI tools that agents can use. AItheGuru.in uses MCP concepts in several of our advanced workflows.