AI Agents in the real world
What AI agents
actually do
in real businesses.
Not "the future of work." Not a 40-slide deck. Just documented cases of AI agents running tasks, cutting costs, and freeing people to do harder things — with enough detail that you can judge whether it applies to you.
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Documented case files
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Business sectors covered
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Learning paths
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Tracking agents since
Latest case files
Services
Booking agent replaced 3 hours of daily admin at a yoga studio
A Berlin-based studio owner connected a scheduling agent to her booking system. It now handles rescheduling, waitlist fills, and confirmation follow-ups without her touching a screen.
E-commerce
Inventory monitoring agent caught a stockout before it happened — twice
A small Shopify store running 400 SKUs set up an agent that watches sell-through rates and flags reorder points. It caught two near-stockouts in the first month that the owner would have missed.
Finance
Invoice chasing agent recovered €4,200 in overdue payments over 60 days
A two-person consultancy had a consistent problem: late payments they were too busy to follow up on. They built a simple agent that monitors invoice due dates and sends escalating reminders. The awkward conversation is now handled before it starts.
The short version
An AI agent is software that takes actions on your behalf — without you supervising each step.
Unlike a chatbot that answers questions, an agent connects to your tools, reads what's happening, decides what to do next, and executes. It might send an email, update a spreadsheet, or trigger a process — based on conditions you set once.
The cases on this site are chosen because they're running in real businesses right now. Not demos. Not pilots. Things that went into production and stayed there.
Start with the basicsWhat agents can do today
- — Monitor data and send alerts when thresholds are crossed
- — Draft and send routine communications based on triggers
- — Move information between systems without manual entry
- — Answer customer questions using your actual documentation
- — Route tasks to the right person or queue automatically
What they still can't do reliably
- — Make consequential decisions without a human in the loop
- — Handle ambiguous or emotionally sensitive customer situations
- — Operate without clear, structured inputs to work from
Learn
Six paths into AI agents — pick the one that matches where you are.
Each path is built around a specific entry point. No prerequisites except curiosity and a working business.
Beginner
What exactly is an AI agent — and is it relevant to me?
Start here if you've heard the term but aren't sure what separates an agent from a chatbot or a simple automation.
5 modules · 40 min total
Beginner
Your first agent: automating one task this week
A walk-through of choosing a single low-risk task in your business and getting an agent running on it in under a day.
4 modules · 35 min total
Intermediate
Connecting agents to tools you already use
How to wire agents into Notion, Google Sheets, email, Slack, or your CRM — without needing a developer.
6 modules · 55 min total
Field Notes
Shorter observations. Things worth flagging.
Not every AI update matters for your business. Field Notes is where we say which ones do — and why.
Failure analysis
Three ways AI agents fail quietly — and how to spot them before they cause damage
Most agent failures aren't dramatic. They're small, consistent errors that compound over weeks without anyone noticing.
Tools
LangChain vs. CrewAI vs. AutoGen: what matters if you're not a developer
A practical comparison of the three most-discussed agent frameworks, written for business owners who want to understand tradeoffs without writing code.
Economics
The hidden costs of running AI agents — what nobody mentions in the demos
API costs, monitoring time, error correction, and the occasional hallucination that requires human cleanup. The full picture before you commit.
Common questions
What people ask before they start.
Not for the use cases covered on this site. The case files here focus on agents that can be configured through existing platforms — tools like Zapier, Make, or agent-native tools like Relevance AI — where the setup is closer to building a form than writing code. You'll need to be comfortable with logic (if this happens, do that), but not programming.
A standard automation follows a fixed script: trigger X leads to action Y, always. An agent can read context, make conditional judgments, and adapt its response. If a customer email is angry, an agent might route it differently than a neutral one. A Zapier trigger just fires the same action every time regardless of content.
It depends entirely on volume and complexity. A simple agent handling 50 emails a day might cost $10–30/month in API usage. A more complex agent running hundreds of multi-step tasks daily could reach $200–500/month. The case files on this site include cost estimates where the business owners shared them, so you can benchmark against similar operations.
All case files are based on documented real implementations. Some business owners asked us not to name their company — we respect that. Where names aren't given, we describe the business type and size precisely enough that you can assess whether the context is comparable to yours. We don't publish speculative or hypothetical implementations.
That's a useful outcome. Not every agent makes sense for every business, and part of what this site is for is helping you make that call clearly. If you read three case files and none feel applicable, the Learn section has a path specifically for assessing whether agent automation is worth your time at all right now.