GUIDE 03 · EXPERT · FREE PDF

AI agents in your business: from idea to launch

Start with one small agent that does one job well.

Agents can take over a specific, repeated job and do it better, faster, and more consistently than a human who has too much else to keep track of.

This guide takes you from zero to your first deployed agent. We walk through how you identify which processes are suitable, how you choose the right tool stack without getting locked in, and how you set up guardrails so that the agent doesn't make mistakes when you aren't around.

No coding is required for most steps. Technical alternatives are presented for those who want to run locally and save on API costs.

38 pages · PDF · Prerequisite: Guide 02 or experience with AI pipelines · Published May 2026

Get the free guide

Enter your email. We'll send the PDF directly.

What you will learn

How to identify agent-ready processes in your company
Choosing a tool stack — local vs. cloud-based
Build a simple research agent (no code)
Build a content agent with Claude or ChatGPT
Guardrails: set boundaries for what the agent is allowed to do
Monitoring: how you know it's going right
Next steps: multi-agent systems for your team

A preview from the guide

Identify the right process

Not all processes are suitable for agents. The classic mistake is starting with what seems coolest rather than what provides the most value. A good agent process has three characteristics: a clear goal, repetitive steps with some variation, and a result that is easy to verify.

Examples from Swedish SMEs: weekly competitive analysis (collect prices, news, reviews), lead follow-up (check LinkedIn, write personalized emails, book meetings), content calendar (fetch news, generate suggestions, schedule).

Checklist: Is it repetitive? Does it have a clear goal? Can you verify the result? Does it take more than 30 min/week manually? If yes to all — it's an agent task.

Choose your tool stack

You have three basic options, and they are not mutually exclusive:

1. Cloud-based, no-code: Claude.ai Projects, ChatGPT Custom GPTs, or n8n Cloud. Fastest to get started. Costs per token or subscription. Best if you don't want to manage infrastructure.

2. Local, with code: Ollama + open models (Qwen3.5, gpt-oss, Gemma 4, Mistral). You pay once, for your GPU. Data stays on your own hardware, which makes GDPR compliance easier. Requires technical setup but gives full control. Best for sensitive data or high volume.

3. Hybrid: A simple local LLM for triggering and routing, a powerful cloud model for critical decisions. What we recommend for most SMEs in 2026.

Your first agent — step by step

We are building a weekly report agent that collects news about your industry, analyzes them, and sends you a prioritized summary every Monday morning.

Tools used: Google Alerts (raw data), n8n (orchestration), Claude API (analysis), Gmail (delivery). Alternatively: entirely local with RSS feeds, Python, and Ollama.

The guide contains complete configuration files, prompt templates and a troubleshooting guide for the most common issues.

The rest is in the guide — including the chapters on guardrails, monitoring, and multi-agent systems.

Get for free →

Is this guide for you?

✓ Yes, if you

Already use AI regularly in your work
Have a repetitive process that costs you 2+ hours a week
Are willing to set up a simple technical solution
Want to save money compared to outsourcing

✗ Not yet, if you

Have never used ChatGPT or Claude seriously
Want a turnkey solution without any effort of your own
Do not have a clear process to automate

Start with Guide 01 instead.