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GUIDE 03 · ADVANCED · FREE PDF

AI agents in your business —
from idea to deployment

Your first agent doesn't need to be complex. It needs to be right.

Agents are the most important technological shift of 2026 for Swedish SMBs. Not because they are cool — but because they do a specific job 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

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What you will learn

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

A preview from the guide

01

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 SME: 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.
02

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 (qwen2.5, llama3, mistral). Pay once — your GPU. GDPR-compliant by design. 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.

03

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.

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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.