AI agents: automation for businesses
AI agents complete tasks on their own – from data analysis to customer communication. Types, costs, tools and practical examples for mid-sized companies.
What are AI agents?
AI agents are software systems that pursue goals on their own, break tasks into sub-steps and use external tools to deliver results without constant human control.
The difference from a chatbot: a chatbot answers a single question. An AI agent plans several steps, retrieves data, makes decisions and adjusts its plan when the situation changes.
Technology
How do AI agents work?
AI agents work in a cycle of four steps that repeats until the goal is reached.
Perception
The agent receives data – an email, a record or a user request – and classifies it.
Planning
A language model (LLM) such as Claude or GPT breaks the goal into sub-tasks and sets the order.
Execution
The agent accesses external tools – APIs, databases, file systems – and carries out each step.
Reflection
After each step the agent checks the result and corrects the plan if something is off.
Categories
What types of AI agents are there?
AI agents can be divided by complexity and ability to learn into four main types.
Reflex agents
React to fixed if-then rules – such as an email filter that sorts messages by keywords.
Model-based agents
Build a model of their environment and make decisions based on likely outcomes.
Goal-based agents
Compare different paths to the goal and choose the most efficient.
Learning agents
Improve through feedback – such as a recommendation system that gets more accurate with every interaction.
Practice
Which tasks do AI agents solve in companies?
AI agents take over recurring, data-intensive tasks that staff used to do manually.
Customer support
Classify enquiries, compile answers and forward complex cases to staff. Combined with AI chatbots in the company this creates automated first-level support.
Data analysis
Create reports from CRM or ERP data and formulate recommendations for action.
Content
Write product descriptions, social media posts or internal documentation based on templates and company data.
Processes
Capture invoices, coordinate appointments or synchronise data between systems. The agent sits inside a workflow automation with AI that connects trigger, data source and action layer into one continuous process.
Implementation
How do you build an AI agent?
You build an AI agent by connecting a language model with external tools and clear instructions.
Narrow the goal
The tighter the focus, the more reliable the result – an agent that is supposed to do everything does nothing properly.
Connect tools
The agent needs access to the systems it works with – email, CRM, databases or APIs.
Write instructions
A system prompt defines how the agent thinks, decides and communicates.
Test and adjust
Test the agent with real tasks, document errors and improve the instructions step by step.
Investment
How much do AI agents cost?
The costs consist of development, platform fees and ongoing API costs.
Development
A simple agent costs €1,500 to €5,000 one-off, complex multi-agent systems €10,000 to €20,000.
Platform
No-code tools cost €20 to €150 per month, self-hosted solutions €30 to €100.
LLM usage
Depending on model and intensity, €2 to €150 per month are incurred – Claude and GPT-4 bill per processed text.
Security
What risks do AI agents have?
AI agents process company data and act on their own – that requires clear rules before the first deployment.
Data protection
Only use GDPR-compliant providers with encrypted connections and server location in the EU.
Access rights
Define which systems and data the agent may access – no more than necessary.
Approval levels
For critical actions such as payments, contracts or customer communication a human has to approve.
Tool stack
Which tools are suitable for AI agents?
The choice depends on technical know-how – from no-code platforms to developer frameworks.
n8n
Open-source platform for workflow automation, self-hostable, over 400 services via drag and drop.
Make
Cloud-based alternative with a visual interface, good for teams without programming skills.
Claude (Anthropic)
Language model with Agent SDK for autonomous agents that read files, run code and control systems.
OpenAI Operator
Carries out web tasks such as bookings, research and form filling on its own.
FAQ
Frequently asked questions about AI agents
For businesses, Claude (Anthropic) is suitable for complex analysis and text work, ChatGPT (OpenAI) with Operator for web tasks and Google Gemini for integration into Google Workspace. The choice depends on the use case.
Chatbots or large language models react to single inputs and deliver one answer. AI agents plan multi-step tasks on their own, access external tools and adjust their approach during execution.
No. No-code platforms such as n8n or Make allow AI agents to be built via drag and drop. Programming skills help with complex workflows but are not a prerequisite.
AI agents process company data and need clear access rules. GDPR-compliant providers, encrypted connections and human approval steps for critical actions reduce the risk.
AI agents take over recurring tasks such as data entry, email sorting or scheduling. Strategic decisions, customer relationships and creative work stay with humans.
Agentic AI describes AI systems that pursue goals, plan tasks and use tools on their own. The term distinguishes these autonomous systems from passive AI models that only respond to individual requests.
Transparency
Sources used
- Own research
- Anthropic: “Building Effective Agents” (2024)
- OpenAI: “A Practical Guide to Building Agents” (2025)
- Google: “Introduction to Agents” whitepaper by Wiesinger, Marlow & Vuskovic (2025)