---
title: "AI Agents: 7 Examples from Everyday Business"
description: "Seven concrete examples of what AI agents handle today: answering calls, detecting deadlines, scheduled tasks, drafts, projects and more."
type: "wissen"
product: "agent"
slug: "ki-agent-beispiele"
source_language: "de"
target_languages: ["de", "en", "es", "pl", "tr"]
published: "2026-06-10"
status: "publish"
faq_json: [{"q":"What are typical examples of AI agents?","a":"Answering calls with a conversation summary, detecting deadlines in documents, recurring tasks on a schedule, email and text drafts, research summaries and agents that carry out entire projects."}, {"q":"Do AI agents work entirely independently?","a":"Within their guidelines, yes – but following the proven principle, external effects such as emails or calls go through the approval of a human."}, {"q":"Which example is suitable for getting started?","a":"Tasks with a clear pattern and no external effect: for example, checking documents for deadlines or creating regular summaries. There the benefit is quickly visible and the risk small."}, {"q":"Do AI agents need access to company data?","a":"Yes – that is the decisive difference from a general chat: an agent that knows the company's contacts, documents and appointments delivers results that fit the business."}]
language: "en"
source_id: "wissen/ki-agent-beispiele"
source_hash: "16441200ba89b5e9a1da68704c9c812161c01c7b57cf278c4e092ba4976068df"
---

AI agents are AI systems that complete tasks independently instead of just answering questions – and they have long been at work in the everyday life of many companies. The following seven examples show what agents realistically handle today: from the phone to documents to entire projects.

## 1. Answer and summarize calls

The most tangible agent of all: an AI phone assistant with its own number that takes calls, answers questions based on stored company knowledge, forwards to a human when needed, and logs the conversation with an AI summary. The team sees in the log who called and what it was about – without listening to a conversation.

## 2. Check documents for deadlines

An agent reads processed documents and detects clearly documented deadlines – such as cancellation or payment deadlines. When implemented well, it works with a minimum confidence level: if it is not sure enough, it does not create anything but reports the case for a decision. This turns a letter into a prepared deadline instead of a forgotten one.

## 3. Handle recurring tasks on a schedule

Reminders, regular analyses, recurring routine tasks: through schedules, an agent works even when no one is chatting with it. It is set up in dialogue – you ask your assistant for a recurring task, and it runs.

## 4. Draft emails and texts

Reply drafts, quote texts, summaries of long threads: the agent prepares, the human reviews and sends. The order matters – following the approval principle, an email only goes out once you have approved the draft.

## 5. Research and condense

Gather, structure and get information to the point – for example, in preparation for a customer meeting: What happened most recently with this customer, which tasks are open, which documents are relevant? An agent with access to the company data answers that in seconds.

## 6. Feed the workflow

Agents recognize in processes and documents what needs to be done and prepare tasks with descriptions and sources. Instead of tasks disappearing into emails and people's heads, they land structured and prioritized in the task list – for approval by you.

## 7. Carry out entire projects

The most advanced form: agents that create files, generate documents and carry out multi-step undertakings in their own working environment – like an employee at their own computer whom you can watch live. "The AI answered" becomes "the AI delivered".

## What all the examples have in common

Two patterns run through them: first, agents need **context** – without access to contacts, documents and appointments, they remain generic text generators. Second, the **approval principle** applies: the agent prepares and assists, but external effects go through the approval of a human.

[webRichtung agent](https://www.webrichtung.de/module/agent/) bundles these patterns as a personal AI employee: with a memory for your company, automations for examples 2, 3 and 6 – and follow-up questions in which you decide before anything goes out. What an agent fundamentally is is explained in the definition article [What is an AI agent?](/de/wissen/was-ist-ein-ki-agent.html)