Understand AI Agents

What Is an AI Agent?

A regular AI chat is like asking a friend a question — you ask, they answer, conversation over. An AI agent is like giving that friend a to-do list, a set of tools, and permission to figure things out on their own.

An AI agent is a program that combines a language model with the ability to take actions. Instead of just generating text, it can browse the web, run terminal commands, read and write files, call APIs, and make decisions across multiple steps — all without you needing to guide it at every turn.

• •A chatbot tells you how to fix a bug — an agent opens your code, runs the tests, finds the issue, and patches it
• •A chatbot suggests a travel itinerary — an agent searches flights, checks your calendar, and drafts the email to your boss
• •A chatbot explains a concept — an agent researches five sources, cross-references them, and produces a cited report

Step 1. Know the Types of AI Agents

Not all agents are the same. Different agents are built for different kinds of work. Here are the main categories you'll encounter:

• •Coding agents — These are the most mature and reliable type. They read your codebase, write and edit files, run tests, and open pull requests. Examples: Claude Code, OpenAI Codex CLI, and Hermes Agent itself. Some can work autonomously for minutes or hours on complex features.
• •Research agents — These search the web, read articles, synthesise information, and produce reports. They can check dozens of sources in parallel and cross-reference claims. Ideal for market research, competitor analysis, or deep-dive learning on a topic.
• •Automation agents — These connect to other tools via APIs. For example, an agent wired into n8n can monitor your email, extract action items, create tasks in your project tracker, and notify you on Slack — all automatically.
• •Desktop agents — These can control your computer — clicking, typing, opening apps. They're useful for automating repetitive UI tasks that don't have APIs. Think of them as a very smart macro recorder.
• •Multi-agent systems — Sometimes you don't want one agent, you want a team. A coordinator agent delegates subtasks to specialist agents (one researches, one writes, one reviews). This is how the most sophisticated AI workflows operate.

Step 2. Understand How Agents Use Tools

The magic of agents comes from tool use. An agent is given a set of tools — functions it can call — and the language model decides when and how to use them. Here are the most common tool categories:

• •File system — Read, write, edit, and delete files. This is how coding agents modify your project and how research agents save their findings.
• •Terminal / shell — Run commands. An agent can install packages, run tests, query databases, or execute scripts — anything you can do in a terminal.
• •Web search — Search the internet and fetch pages. This lets agents pull in real-time information beyond their training data.
• •API calls — Interact with other services. An agent can send emails, create calendar events, post to social media, or query your CRM.
• •Browser control — Navigate websites, fill forms, click buttons. This turns any web app into something an agent can operate.

The agent doesn't just blindly call tools — it thinks, plans, observes the results, and adjusts. Here's a real example of what the agent's internal loop looks like:

Step 3. Run Agents Locally with Your Own AI

You don't need to pay for cloud AI to use agents. You can run them entirely on your own machine with open-source models. Here are the best options:

• •Hermes Agent — A full-featured agent framework that runs on Ollama (local) or cloud providers. It comes with tools for file editing, terminal access, web search, and browser control. Perfect for a daily AI assistant that respects your privacy.
• •Open Interpreter — A simpler tool that gives an LLM access to your terminal. Great for one-off automation tasks, data analysis, and system administration. Install with pip and point it at any local model.
• •n8n + AI nodes — The n8n automation platform has built-in AI agent nodes. You can build visual workflows where an agent processes incoming data, makes decisions, and triggers actions across hundreds of services.

For local agents to work well, you need a capable model. A 7B–14B model (like Qwen 2.5 14B or Mistral 7B) handles tool calling reliably. Smaller models (3B and under) tend to struggle with the multi-step reasoning agents require.

Step 4. Stay Safe — Agents Have Real Power

This is important: agents can actually do things. Unlike a chatbot that only produces text, an agent can delete files, send emails, spend money via APIs, or modify your website. The power is real, so the caution should be too.

• •Start with read-only tools — Let an agent search the web and read files before you give it write access. Build trust gradually.
• •Use confirmation modes — Most agent frameworks (including Hermes Agent) let you require human approval before destructive actions like deleting files or making network calls. Turn this on.
• •Run in a sandbox — For coding agents, work in a dedicated project directory or a Docker container. That way even if the agent goes off-script, it can't touch anything important.
• •Never give agents unrestricted API keys — If an agent can call your email provider's API, use a key with minimal permissions. Don't give a research agent the ability to delete your entire inbox.
• •Review agent output before acting on it — Agents can hallucinate, just like chatbots. An agent might claim it "sent the email" when it actually errored out. Always verify important actions.
• •Log everything — Good agent frameworks keep a detailed log of every action taken. Review these logs periodically to understand what your agents are actually doing.

Step 5. Get Started with Practical Examples

The best way to understand agents is to use one. Here are three beginner-friendly tasks to try. Each one teaches a different aspect of agent behaviour.

• •Research task — Ask an agent: "Research the top 5 privacy-focused alternatives to Google Analytics. For each one, find out if it's open-source, where it's hosted, and what it costs. Save the results to a markdown file." This exercises web search, reading, and file writing.
• •Code review — Point an agent at a small project and ask: "Review the code in this directory for security issues and code quality problems. List your findings with file names and line numbers." This exercises file system access and analysis.
• •Document summarisation chain — Give an agent a folder of documents and ask: "Read every PDF in this folder, summarise each one in a paragraph, then write an executive summary that ties them all together." This exercises multi-step planning and synthesis.

Start with research tasks — they're the safest. The agent can only read from the web and write to a file. Once you're comfortable, graduate to tasks that modify files, and eventually to agents that can run commands.

Step 6. Build Your First Agent Workflow

Ready to go beyond one-off tasks? Here's how to set up a recurring agent workflow that runs on autopilot. We'll use n8n as an example because it's the most visual way to start.

• •Trigger — A schedule (e.g., every Monday at 9 AM) or an event (new email arrives, RSS feed updates).
• •Agent node — The AI agent receives the trigger data and a prompt ("Summarise this week's industry news from these RSS feeds, draft a newsletter, and save it as a draft in my email").
• •Tool calls — The agent executes: fetches RSS feeds → summarises with local LLM → drafts email → saves to drafts folder.
• •Human review gate — Before the final step, the workflow pauses and sends you a notification. You review the draft and approve or edit it.

This pattern — trigger → agent → tool calls → human review → action — is the foundation of safe, productive agent automation. Start with a simple version of this and expand as you gain confidence.

Quick Tips

• •The quality of your prompt matters more for agents than for chatbots. Be specific about what the agent should do, what tools it has, and what "done" looks like.
• •If an agent gets stuck in a loop (trying the same broken approach repeatedly), stop it and give clearer instructions. Agents can't always recognise when they're going in circles.
• •For complex tasks, break them into smaller agent runs. Instead of "build the whole website," try "build the homepage," then "add the blog," then "add the contact form."
• •Local agents with a 14B+ model can handle surprisingly complex tasks. Many people assume they need GPT-4 for agents — they don't. A good local model with the right tools is formidable.
• •Keep a "lessons learned" document. Every time an agent surprises you (good or bad), write it down. Over time you'll develop an intuition for what tasks suit agents and how to prompt them.