AI agents: how to use the smart helpers for your business

AI agents are in the process of fundamentally changing our business processes. They automate tasks, analyze data and make decisions - around the clock, without a break. It's no wonder that more and more companies are turning to this intelligent technology to work more efficiently and reduce costs.

Whether in customer service, sales or internal organization - AI agents take over repetitive tasks, improve workflows and relieve employees. Do you run a customer service department? Then you know the challenge: hundreds of inquiries are received every day - from product questions to complaints. An AI agent can process these inquiries automatically, provide the right answers and only pass on really tricky cases to your team. This saves time, reduces costs and increases customer satisfaction.

But the potential applications go far beyond this. AI agents also help you to optimize workflows, improve decision-making processes and secure competitive advantages. Getting to grips with this technology now will lay the foundations for more efficient and innovative work.

In this guide, you will find out what AI agents are, how they work and how you can use them specifically for your purposes - from implementation to economic and security-related aspects. Take a look into the world of intelligent automation with us!

Before we get to the potential, let's first clarify the basics. What are AI agents anyway? Basically, it can be said that intelligent helpers are software solutions based on artificial intelligence. They are able to carry out tasks independently, learn and react to changes. They use machine learning to recognize patterns in data and make decisions based on these findings. This enables them to act without constant human guidance.

An AI agent can create personalized product recommendations based on the preferences of the user of an online store. The following graphic shows how the basic process works:

This example clearly shows how an AI agent creates data-driven individual emails with product recommendations and thus specifically addresses the interests of the online store's customers in order to optimize their shopping experience.

The term "agentic AI " is increasingly being used in the discussion about AI agents - but what exactly does it mean and how does this approach differ from conventional generative AI?

From generative AI to agentic AI

Generative AI - as we know it from tools such as ChatGPT, DALL-E or Midjourney - is designed to generate content: text, images, code or music. However, the model acts reactively: it only generates answers when prompted to do so by humans. It has no memory, does not make its own decisions and cannot act independently.
Agentic AI goes much further: it transforms generative models into active, autonomous agents that can act independently - with a goal, a plan and the ability to make decisions.

What makes Agentic AI special?

Agentic AI is more than just an intelligent chatbot. It combines classic components of AI agents (perception, decision, action, learning) with advanced capabilities such as

• Multi-step planning: agents independently plan multi-step tasks.
• Tool usage: They actively access databases, APIs or internal systems.
• Memory: They retain information over longer periods of time and learn from it.
• Autonomy: They can act without a direct prompt - e.g. when a certain condition occurs.

The architecture of an AI agent is divided into three central components, which together ensure the functionality and efficiency of the system: the language model, the orchestration layer and the tools.

The language model forms the heart of an AI agent. It is not only designed tofollow simple instructions, but also to draw logical conclusions, plan complex tasks and process multimodal input such as text or images.

Modern language models can be adapted to specific requirements in order to master industry-specific challenges, for example. They are the central unit that decides which steps the agent should take next and serve as the system's "thinking engine".

For example, an AI agent used in a logistics company uses its language model to understand customer requests and suggest the best delivery options based on real-time data.

The orchestration layer is the control center that coordinates all of the AI agent's processes. It is responsible for recording information, managing internal processes and initiating actions based on results. Memory, states and planning are managed here in order to make the agent flexible and adaptable.

A key feature of the layer is that it uses prompt engineering frameworks. It uses these to specifically control the behavior of the agent. From simple rules to complex, networked decision trees - the orchestration layer ensures that all of the agent's components work together efficiently.

Tools serve as a bridge between the AI agent and its environment. They enable it to go beyond its inherent capabilities and access external data sources or services. Examples of such tools are

• Databases to retrieve and store information.
• APIs to access external applications such as weather services or CRM systems.
• Specialized extensions, such as analysis tools that meet industry-specific requirements.

Interaction of the components

The interaction of the three components - language model, orchestration layer and tools - makes the AI agent a powerful system. While the language model makes decisions, the orchestration layer ensures the logical sequence of actions, and the tools expand the agent's possibilities through practical interactions with the outside world.

The cooperation between the individual components enables the AI agent to act not only precisely, but also flexibly and in a variety of ways - a decisive advantage in business processes that are becoming increasingly dynamic.

The cognitive architecture we have presented to you enables AI agents to iteratively analyze and process information and make decisions.

Instead of giving static answers, they react dynamically to incoming information and continuously adapt their actions. The focus here is on the ability to act not only on the basis of training data, but also to incorporate external data sources and tools in order to achieve more precise results.

Let's take a look at a practical scenario: An agent used for IT troubleshooting receives a user request, analyzes logs with the help of tools, identifies possible error sources and finally offers a well-founded solution.

One particular example of how an AI agent works is the ReAct framework. Here, the agent acts by documenting each step in a sequence of considerations ("Reason"), actions ("Act") and observations ("Observation") and plans the next steps based on this:

This iterative approach enables the agent not only to solve complex tasks, but also to explain the process. One advantage of this approach is that it makes it transparent how decisions are made - a decisive factor for trust and acceptance by users.

Chain of Thought (CoT)

The Chain-of-Thought framework structures the model's decision-making process by making intermediate considerations visible. The model runs through a task in several steps and thus creates more transparency and attention to detail in problem solving.

• Example: An agent that solves complex mathematical problems explains each step in detail, from the definition of the problem to the calculation of the solution.
• Extensions: Techniques such as self-consistency or multimodal CoT allow different types of input (e.g. text and images) to be processed efficiently.

Tree-of-Thoughts (ToT)

This framework is particularly suitable for strategic tasks. It enables the model to evaluate different solutions simultaneously before making a final decision. A typical area of application is the development of business strategies, in which various scenarios are examined and weighed up.

Extensions

Extensions act as an interface between AI agents and APIs. They standardize the interaction so that agents can use APIs independently of their specific implementations.

• Example: An extension that allows the agent to retrieve weather data from an API and make recommendations for logistics based on this information.

Functions

Functions are specific code modules that agents can use to perform defined tasks. These modules are particularly valuable as they enable developers to structure outputs in a targeted manner and integrate them into existing systems.

Data stores

Agents use data stores to access up-to-date information that goes beyond the original training data. With the help of vector databases, they can extract relevant data and incorporate it into the decision-making process.

• Example: An agent in an e-commerce system uses a data store to retrieve real-time information about stock levels and answer customer queries accordingly.

In-context learning

Agents learn to use tools "on the fly" by receiving examples and instructions directly in the prompt. This approach is particularly flexible and is well suited to dynamic tasks.

Retrieval-based in-context learning

Here, the model is supplemented by external memories that dynamically provide relevant information and examples. This enables agents to access both current knowledge and historical data.

Fine-tuning

For long-term improvement, models can be trained on specialized data sets through fine-tuning. This gives the agent a better understanding of specific tasks and the targeted use of tools.

The versatility and flexibility of AI agents result from the clever combination of cognitive processes, frameworks and tools. They are not only designed to process data, but also to develop solutions intelligently and comprehensibly - a big step towards automation and increased efficiency in your day-to-day business.

The first step in introducing AI agents is to identify the right processes that can be automated or supported. Start with simple, repeatable tasks where AI can quickly add value. A small pilot project will show you how well AI agents work in your company.

An AI agent can optimize and automate your business processes - but only if it is planned and implemented correctly. Through step-by-step development, testing with users and continuous optimization, it becomes more and more powerful.

In the next section, we will show you examples from various industries in which AI agents are already being used successfully.

AI agents are already proving in many industries that they can optimize processes, increase productivity and improve customer satisfaction. Here are some examples from various sectors.

Customer service (e-commerce and retail)

In the e-commerce industry, AI agents are increasingly being used to automate and improve customer service. AI chatbots can answer queries around the clock, track orders and answer frequently asked questions (FAQs).

Example: Salesforce Einstein uses AI to make personalized recommendations to customers and provide them with a tailored shopping experience. The AI agent learns from interactions and offers customers relevant products based on their preferences and behavior.

Financial services (banking and insurance)

In the financial sector, AI agents are often used to improve communication with customers and answer complex financial questions. This includes providing account overviews, processing inquiries about financial products or managing insurance claims.

Example: An AI agent in a bank can support users with budgeting in real time by analyzing expenses and making suggestions for savings. AI-supported chatbots also offer help when applying for loans or submitting claims to insurance companies.

Healthcare (hospitals and clinics)

In the healthcare sector, AI agents answer patient queries, analyze medical data and manage appointments. They can also refer patients to medical professionals if more complex problems arise.

Example: IBM Watson Health is used by healthcare facilities to integrate AI-supported agents into patient support. These agents can analyze patients' symptoms, make recommendations and provide initial diagnoses.

IT and software development

AI agents are used in software development and IT support to automate bug fixes, software updates and responses to technical queries. They make it possible to identify system problems and fix them before they even become visible to the end user.

Example: Salesforce Service Cloud uses AI agents to automatically categorize and prioritize support tickets. These intelligent assistants can immediately analyze technical requests and offer solutions without the need for a human support employee to intervene.

Logistics and supply chain

In the logistics industry, AI agents help to optimize deliveries, monitor inventories and predict delivery times. AI systems can react to changes in the supply chain in real time and offer solutions to avoid bottlenecks.

Example: Kiva Systems, now part of Amazon Robotics, uses AI agents to run warehouses more efficiently. They analyze orders and coordinate autonomous robots to bring products to the right place in the warehouse at the right time.

Human resources and recruiting

AI agents can revolutionize the recruitment process by scanning applications, assessing candidates and automating interviews. They offer HR departments a faster and more objective method of selecting talent.

Example: HireVue uses AI agents to analyze and evaluate candidate interviews. The smart assistant assesses aspects such as language, body language and answers to questions in order to optimize the recruitment process and select the best candidates.

AI agents already offer a wide range of applications. They can be adapted according to industry and company requirements. Their ability to automate tasks, analyze data and make well-founded decisions already makes them a valuable tool in day-to-day business.

Artificial intelligence is on the cusp of fundamentally changing the way we will work in the future. By taking over many routine tasks, resources will be freed up, allowing employees to focus on more strategic and creative tasks. At the same time, new fields of work are emerging, particularly in the development, monitoring and optimization of AI-supported processes.

Transformative impact on central business areas
AI agents will play a key role in data analysis in particular. They are able to process large volumes of data in the shortest possible time, recognize patterns and provide well-founded recommendations for action. This opens up completely new possibilities for optimizing decision-making and exploiting market opportunities more efficiently.

AI agents also promise ground-breaking potential in the area of customer service. By integrating them into chatbots and service platforms, they can not only answer standard inquiries automatically, but also offer personalized solutions that specifically address the needs of individual customers, resulting in greater customer satisfaction and loyalty.

Product development also benefits from intelligent automation. AI agents can predict trends, analyze feedback and suggest innovative product ideas, which speeds up the development process and makes it more targeted.

Future trends in the use of AI agents
Important developments are already emerging today: one trend is the refinement of machine learning, which allows AI assistants to achieve even more precise and context-specific results. Finally, hyper-personalization will play a central role in customer service, with AI making it possible to offer individually tailored solutions for each customer.

Making intelligent use of opportunities
The introduction of AI assistants not only offers you the opportunity to work more efficiently, but also to secure competitive advantages and open up new business areas. Those who rely on this technology at an early stage benefit from cost savings and strengthen their innovative power in the long term. Artificial intelligence is not just a tool, but a key driver for the future of our economy.