Everyday routines and workflows: Do it yourself, automate it classically, or leave it to AI agents? – Image: Xpert.Digital
Executing multi-stage workflows is one of the key aspects – but what's really interesting is how they do it
From chatbot to autonomous employee: How AI agents are revolutionizing our work
For a long time, when we thought of artificial intelligence, we primarily thought of clever chatbots. We asked a question, the AI gave an answer. We entered text, the AI translated it. This interaction was a ping-pong game: one input led to a direct output. But the technology has evolved. The latest and perhaps most important leap in AI development is the emergence of so-called AI agents.
Executing multi-stage workflows is one of these agents' core capabilities – but what's truly fascinating is how they do it. To understand why AI agents are currently revolutionizing the world of work, we need to look at what distinguishes them from traditional computer programs.
Related to this:
- Say goodbye to rigid scripts: How autonomous AI agents are taking over entire workflows in companies
The difference between automation and autonomy
Traditional software programs or scripts can, of course, also execute multi-stage processes. This is often called automation or RPA (Robotic Process Automation). However, this type of automation is rigid and rule-based.
If you give a classic script the command: "Do step A, then step B, then step C," it will do exactly that. Rigidly, without looking left or right. If an unexpected error occurs during step B—for example, because a website has changed its layout or a file is in the wrong location—the program stops. It throws an error message and waits for a human to solve the problem.
Instead, you simply give an AI agent a goal. For example, you could say: "Research the current market trends for electric cars in Germany, compare the sales figures of the three largest manufacturers, and create a summary with a chart."
The agent does not receive detailed step-by-step instructions. It independently determines which steps (workflows) are necessary to achieve the goal. It breaks down the large task into small, manageable subtasks and plans them dynamically. Therefore, it acts in a goal-oriented manner and not according to rigidly programmed rules.
Automate research: Run projects in the background
This represents a massive change for our daily work. With AI agents, we can fully automate complex research and allow projects to continue running in the background with just a single input.
Imagine you're an analyst, marketing expert, or project manager. Until now, conducting a comprehensive market analysis required hours spent in front of a screen. You had to enter various Google search queries, skim countless articles, filter out irrelevant information, collect data in an Excel spreadsheet, analyze that data, and finally compile everything into a presentation. This is time-consuming, monotonous, and ties up valuable resources.
With an AI agent, this process changes fundamentally. You give your starting command, formulate your goal clearly and precisely – and then you sit back. The agent takes over. While you attend to other, more important tasks, participate in a meeting, or even leave work for the day, the agent continues to work tirelessly in the background.
He performs the necessary searches, reads through hundreds of pages, compares sources, filters the important from the unimportant, extracts the relevant data, and prepares it. You no longer have to control or initiate every single step. When you open your laptop the next morning, the finished, structured result is waiting for you. The agent has transformed what used to be a tedious, hours-long task into a process that only took you a minute to place the order.
External tools: The agent accesses the world
How is this technically possible? A crucial factor is that AI agents are not limited to their internally trained knowledge. A language model like ChatGPT (in its early versions) only knew what it had been trained to know up to a specific cut-off date. It couldn't look up the weather forecast or the current stock price live on the internet.
However, modern AI agents can use external tools in their multi-stage workflows. They can:
- Search the open internet and retrieve live data.
- To use a calculator to solve complex mathematical equations without errors.
- Write and execute code directly, for example to analyze data or generate charts.
- Access internal company databases or APIs.
- Send emails independently or enter appointments into a calendar.
This ability to use tools is what truly transforms the agent into a digital employee. They are no longer confined to their text box, but can interact with the digital world.
The magic of the ReAct principle: Thinking and acting
That is perhaps the greatest magic of agents. They often operate according to the so-called ReAct principle, a neologism combining "reason" (thinking/reasoning) and "act" (acting). This process mimics human problem-solving remarkably well.
Let's walk through a concrete example: Your agent has been tasked with finding out the market shares of electric car manufacturers for the current quarter.
- Planning: The agent decides on the first step.
- Act: He uses his search tool and searches the internet for "E-car market shares Germany Q1 current year".
- Observe: He reads through the search results he found.
- Reasoning: He analyzes the information and concludes: "The result contains numbers, but the article is three years old. This source is outdated and does not help me achieve my goal."
Now the major difference to simple automation becomes apparent. Instead of simply ignoring this error, outputting an incorrect result, or aborting with an error message, the agent adjusts its multi-stage workflow. It reflects on its own intermediate result.
He thinks to himself, "I need to formulate my search query more specifically." He tries again (Act) with a new query, perhaps specifically on the website of the Federal Motor Transport Authority. He evaluates the new results (Reason) and only continues working when he has found the correct, up-to-date information. He is therefore self-checking.
The agent's memory
As the agent works through this complex, multi-stage process – which can sometimes involve dozens or hundreds of intermediate steps – he remembers the entire context so far. He never loses the thread.
When he reaches step 15 and is supposed to draw the diagram, he still remembers exactly why he rejected a particular data source in step 2 and selected a different one in step 5. He has the entire process stored in his memory and can use this knowledge to make the final decisions and produce a coherent overall result.
Related to this:
AI as a game-changer for workforce forecasting: The AI chapter shows that generative AI could save around 3.9 billion working hours by 2030 – which would close over 90 percent of the demographic gap of 4.2 billion hours. Current forecasts of skilled labor demand are considered potentially obsolete because they barely factor in the productivity effect of AI.
The new era of work
The fact that AI agents can handle multi-stage workflows is what makes them so incredibly useful to us in everyday life. They take the tedious work off our hands and give us back our time.
But what makes them so technologically interesting and revolutionary is their ability to independently plan and execute these workflows, adapt flexibly to errors, and find the appropriate external tools. They act in a goal-oriented rather than rule-based manner. Anyone who understands how to set a clear goal for an AI agent can drive entire projects forward in the background while focusing on strategy and creativity. The transition from a mere assistance system to an autonomous workforce has only just begun.
Does the tone of voice suit your target audience, or should certain technical terms be further simplified or explained in more detail?
Your global marketing and business development partner
☑️ Our business language is English or German
☑️ NEW: Correspondence in your native language!
Konrad Wolfenstein
I and my team are happy to be available to you as your personal advisor.
You can contact me by filling out the contact form here wolfenstein@xpert.digital:or simply call me at +49 7348 4088 965. My email address is
I'm looking forward to our joint project.
