An AI agent is a system that not only answers questions but also independently performs tasks. While a regular chatbot waits for input and provides an answer, an agent can plan multiple steps, use tools, and make decisions.

Example: You say, “Create a monthly report from our sales data.” A chatbot would explain how to do that. An agent opens the database, retrieves the figures, calculates key metrics, creates graphics, and delivers the finished report.

Chatbot: Reacts to input, provides answers, has no access to external systems. Each message is processed in isolation.

Agent: Plans independently, uses tools (databases, APIs, email, calendar), executes multi-step workflows, and can evaluate intermediate results and adjust its course.

The crucial difference is agency. A chatbot talks. An agent acts. The next generation of AI solutions are not better chatbots – they are agents that take on real work.

Tools are external functions that an agent can call to interact with the real world. The language model alone can only generate text – with tools, it can retrieve data, perform calculations, send emails, or create files.

Typical tools include: web search, database queries, code execution, calendar management, CRM access, file system operations. The tools an agent possesses determine its capabilities. Tools can be thought of as a craftsman’s instruments – the expertise is in the mind (model), but without tools, it remains theoretical.

Sub-agents are specialized agents deployed by a superordinate agent for specific subtasks. Instead of a single agent doing everything itself, it delegates tasks to specialists.

Imagine a project manager (main agent) who assigns tasks to a research specialist, a data analyst, and a copywriter (sub-agents). Each sub-agent has its own tools, instructions, and area of expertise. The main agent coordinates and assembles the results.

A multi-agent system consists of several agents working together to solve complex tasks. Each agent has a defined role, specific capabilities, and its own instructions.

Example of an agent team in customer service:

• Router Agent: Analyzes the request and forwards it to the correct specialist.
• Product Agent: Answers questions about products and services.
• Complaint Agent: Processes complaints according to defined escalation rules.
• Technical Agent: Assists with technical issues and consults the knowledge base.

Multi-agent systems are more powerful, maintainable, and reliable than individual, overloaded agents.

MCP is an open standard, developed by Anthropic, that defines how AI models communicate with external data sources and tools. MCP can be thought of as a kind of “USB standard for AI” – a unified interface through which various systems can be connected.

Without MCP, every AI integration must be programmed individually. With MCP, tools, databases, and services can be connected once according to a standard and then used by various AI systems. This massively reduces development effort and increases flexibility.

Function Calling (also known as Tool Use) is the ability of a language model to call external functions in a structured way. The model decides, based on user input, which function to call with which parameters.

Example: You ask, “What will the weather be like tomorrow in Zurich?” The model recognizes that it needs to call a weather API, generates the correct parameters (location: Zurich, date: tomorrow), and processes the result into a natural response. Function Calling is the technical basis for AI agents to act.

An Agentic Workflow is a work process in which AI agents independently go through multiple steps – including planning, execution, review, and correction. Unlike a simple question-and-answer process, the agent can assess its own progress and change course if necessary.

Example: “Analyze our last 50 customer complaints, categorize them, identify the top 3 problems, and propose a solution for each.” An agent plans this task, executes each step, checks intermediate results, and ultimately delivers a structured outcome.