Home AI What does a (business) AI agent do?

What does a (business) AI agent do?

AI, AI
AI, AI
August 27, 2025
12 Min read

Dr. Andreas Wagner

Customer Success Executive

This article describes the definition, benefits and functionality of AI agents. We explain the central building blocks of an AI agent, its technological implementation as well as governance and risk management, which are crucial for its successful use in companies. AI agents represent a milestone in digital transformation and will have a far-reaching impact on all business processes.

Table of contents

1. introduction

Let's do a thought experiment: We have developed an AI agent and call it Yvonne – based on the fact that Yvonne previously took on exactly these tasks with her marketing team. Now this digital agent is taking over her role. Let's take a look at how Yvonne works and what skills she has.

Yvonne is our new marketing agent. Her task is to increase the sales of product X in Europe by 10% within three months. Yvonne is able to break this complex goal down into a series of concrete steps and act autonomously:

It researches target audiences in the most important European markets.

It analyzes the competition to identify the best strategies.

It develops a customized marketing strategy for each country.

It creates and publishes targeted advertisements on the most suitable channels.

It continuously monitors sales and adjusts the strategy as needed.

Yvonne accesses various resources for implementation:

It accesses market research databases and advertising platform APIs.

It captures personalized ad copy and implements campaigns in the appropriate languages.

It launches campaigns on platforms like Google Ads and social media.

A key aspect of Yvonne's ability is her autonomy. She continuously monitors click-through and conversion rates. If a campaign in Switzerland is not performing as expected, she independently terminates it and launches an optimized version.

Yvonne is an AI agent. Just a few years ago, that was pure science fiction – today it is reality. Companies that do not use AI agents soon risk considerable competitive disadvantages.

1.1 What are AI agents?

An AI can human abilities such as logical thinking, problem solvingsolving uand decision makingen. AI agents connect these capabilities with the ability zto act autonomously, to concrete goale to achieve concrete goals.

The key features of AI agents are:

Autonomy: They make decisions without continuous human intervention.

Goal orientation: Breaking down an overarching goal into smaller steps

Interaction with the environment: It learns from its experiences to solve future tasks more efficiently.

The methods for creating AI are divided into two categories: 1. rule-based AI (Good Old-Fashioned AI) and 2. the "connectionist" approach (machine learning).

In the approach of rule-based AI approach clear, human-programmed if-then rules and symbolic logic are used. AI draws conclusions by applying these rules. It is ideal for problems with fixed, known rules, such as in expert systems or for processing structured data.

In the approach of "Connectionist AI" uses machine learning, in particular neural networks. AI learns to independently recognize patterns and relationships in large amounts of data instead of following explicit rules. It is ideal for complex, unstructured problems such as speech recognition, image analysis or text generation. It merely recognizes patterns and calculates probabilities for the next part of a sentence; a connectionist AI imitates, it does not "understand".

OpenAI is a prominent example of the connectionist approach. Their models, such as GPT ("Generative Pre-trained Transformer"), are based on deep learning, a specialized form of machine learning. The AI learns from vast amounts of text and image data to perform complex tasks such as writing texts or generating code. It was not programmed with fixed rules, but enabled by training with data to recognize patterns and work creatively.

2. the technological core: how AI agents work

The function of an agent is based on the so-called «"Agentic-Loop."The Agentic-Loop process consists of five steps:

1. Perception

The agent gathers information from its environment, such as from websites or databases.

2. Planning

A language model (LLM) serves as the brain to create a detailed plan.

3. Tool usage

The agent uses various tools such as APIs, web browsers, or databases to expand its capabilities.

4. Execution

The planned steps are implemented using these tools.

5. Reflection

The agent evaluates the result and adjusts its plan as needed to achieve the goal.

AI agents are AI systems, meaning they are composed of various components («Compound Systems»). The combination of different components allows an agent to evolve from pure language processing to an autonomous, acting system.

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2.1 The brain: Large Language Model (LLM)

The Large Language Model (LLM) is the cognitive center of any modern agent. Think of it as the chief planner and problem solver. An LLM receives a complex task, such as "Book a trip to Rome and find the best restaurants". It is able to break down this task into a logical sequence of actions: first hotel search, then flight search, then restaurant research. It is trained to understand complex relationships and draw logical conclusions. If a booking attempt fails, the LLM can analyze why and suggest an alternative plan.

Techniques like Chain-of-Thought (CoT) make the LLM's thought process visible. Instead of just delivering the final solution, it shows the individual logical steps. Tree of Thoughts (ToT) goes even further by tracking several possible thought paths in parallel and selecting the best one, similar to a chess computer that calculates different moves in advance.

2.2 The RAG method: insider knowledge for the AI agent

An LLM has been pre-trained on a huge amount of data. It is able to predict text modules amazingly well, but (hopefully) has no insider knowledge of your business processes. An AI agent can draw on certain predefined knowledge stores. First of all, the RAG (Retrieval-Augmented Generation) method is often used. RAG is a method that can be used to expand an agent's knowledge without having to retrain the LLM. It is like allowing a student to use a textbook during an exam.

The process works as follows: First, external documents (such as PDFs, internal wiki pages or business reports) are converted into vectors using embedding models and stored in a vector database. A vector is a list of numbers that represents the meaning of the text. Semantically similar texts (e.g. 'sunflower blooms' and 'flower grows in the sun') are very close to each other in a vector space.

When a question is asked, the agent uses «Vector-Search» to find the most relevant documents. The LLM then receives the original question plus the found documents to generate a precise, fact-based answer. In addition to the known prompt, the LLM receives further context. This significantly reduces “hallucinations” (fabricated information) of the LLM.

2.3 The action level: tools of an AI agent

An agent can think, but in order to act, it needs tools. These tools are the interface to the outside world. They are usually available in the form of APIs.

Examples of tools that an AI agent can use include:

Browser tool: The agent can search the web to find current information.

Email tool: The agent can read or send emails to communicate with people or systems.

Company API: The agent can access internal systems to, for example, query a customer status or process an order.

Code interpreter: The agent can write and execute code to perform complex calculations or analyze data.

2.4 The five maturity levels of AI agents

AI agents are divided into different maturity levels. AI agents differ in how independently they gather knowledge, how autonomously they make decisions, and how effectively they can learn.

Reactive AI Agents

are simple, rule-based agents that react directly to their current environment without remembering past actions. They have no long-term plan and use simple If-Then rules («Condition-Action-Rules») to trigger an action. Example: A robot vacuum cleaner that turns around as soon as it touches a wall.

Model-based reactive agents

have a model of their environment based on their history of perception. They can assess the current state of the world even when not all the information is directly visible and make more informed decisions. Example: A self-driving car that adapts its speed to the weather conditions, even if the road is currently dry.

Goal-based AI agents

plan a sequence of actions to achieve a specific goal. In doing so, they not only consider the current status, but also how different actions can lead to a desired goal. They use search and planning algorithms. Example: A chess program that plans its moves in order to checkmate its opponent.

Usage-based AI agents

have multiple possible goals and choose the action with the highest benefit. They not only strive to reach the goal, but to reach it in the best possible way. They achieve this through advanced «Reinforcement Learning» techniques. Example: A navigation system that not only finds the fastest route, but also one that optimizes traffic jams, tolls and fuel consumption.

Learning AI agents

can improve themselves. They learn from their experiences and adjust their rules, model, or utility function accordingly. They have several components: a learning algorithm, a performance element (that selects the actions), a critic (that gives feedback), and a problem generator (that creates the new experiences). For example: An AI system that learns from thousands of games to improve its performance.

3. characteristics of a Business AI agent

Business AI agents differ fundamentally from general AI agents. Their main purpose is to optimize business processes and improve financial metrics. To do this, they focus on specific, business-relevant tasks.

Business AI agents are not developed as stand-alone, isolated systems, but to solve specific problems within value chains. Examples of this are the automation of customer inquiries, the optimization of supply chains or the personalization of marketing campaigns.

In contrast to generic AI models that are trained on public data, business AI agents primarily use proprietary data. This data represents the actual competitive advantage as it contains specific knowledge about customers, products, operating processes and markets. By processing this proprietary information, agents can make highly specialized and context-sensitive decisions that directly target business objectives. The governance of this data is crucial for security, intellectual property protection and compliance.

For their successful deployment, Business AI agents must be seamlessly integrated into the existing IT landscape. This includes connecting to systems such as CRM, ERP, or SCM. Through this integration, the agents can retrieve data in real time and execute actions directly in the relevant business applications. A cross-platform architecture and standardized APIs are crucial technical requirements for this. An important role is played by the MLOps strategy to ensure smooth deployment, monitoring, and maintenance of the agents.

4. the creation and use of Business AI agents

Developing Business AI agents requires special considerations, as the AI directly intervenes in business processes.

4.1 The technical implementation of Business AI agents

Besides achieving the functional goals of an AI agent, security, reliability, and scalability are crucial.

Proprietary, i.e. proprietary, data is the most important input for a business AI agent. To be able to use this data successfully, securely and in a sufficiently scaled manner, a company needs a modern data platform. A business AI agent requires considerable computing power to perform complex tasks and process large volumes of data. The data platform must be horizontally scalable in order to keep pace with the growth of the company and the increasing number of tasks processed by the agent. It must ensure high performance in data processing, especially when using GPUs to meet real-time requirements. The platform must serve as a central data source. A Lakehouse architecture combines the flexibility of a data lake for unstructured data with the governance and management functions of a data warehouse. This allows the AI agent to access all relevant company data - from structured tables to unstructured text and images - from a single location.

The Model Context Protocol (MCP) and LangChain together play a vital role in the development of Agentic AI. The MCP is an open standard that allows AI models to access external tools and data sources. Think of it as a "USB-C port" for AI. It standardizes communication, allowing agents to query the real world via APIs, retrieve data from databases, or perform specific actions in other systems without needing a separate, custom integration for each connection. This is crucial for AI agents to act beyond their internal knowledge and perform dynamic, context-related tasks. LangChain is a development framework that provides developers with the tools to control an agent's logical thinking processes. It's the "software architecture" that connects the various building blocks, such as prompts, models, and data connectors. With LangChain, developers can define complex workflows in which an agent processes requests step by step, uses external tools, and ultimately generates a coherent response.

While the Model Context Protocol (MCP) provides the technical interface to use external data and tools, LangChain decides when and how this interface is used. LangChain structures the process in which an agent accesses external resources supported by the MCP standard. In short: MCP enables the connection, while LangChain orchestrates the intelligence and flow to use this connection.

A robust security architecture is based on role-based access control (RBAC). This ensures that each agent can only access the data and systems that are absolutely necessary for its specific task. This is complemented by secure authentication methods such as OAuth 2.0 or robust API keys, which verify the identity of the agent before each action. In addition, all data processed by the agent must be encrypted both during transmission and when stored. Finally, auditability of every action of the agent in secure logs is required for complete tracking and verification.

A business agent must remain stable and functional even in the event of unexpected errors in the IT landscape. This requires comprehensive error handling and logging in the code to enable rapid analysis and resolution. In the event of temporary errors, the agent should automatically repeat the action with an exponential backoff strategy, i.e. carry out automated retries. To prevent repeated calls to a faulty system, the circuit breaker pattern can be implemented. This is supplemented by continuous monitoring and alerts, which monitor the availability and performance of the agent and its dependent systems in order to notify the operations team immediately of critical errors.

In addition, a business solution must be able to scale with the growth of the company and be able to handle an increasing number of tasks. The technical architecture should therefore be designed from the outset for scalability designed for scalability, for example through a microservices architecture. This makes it possible to scale and maintain individual components independently of each other. Through containerization and orchestration with technologies such as Docker and Kubernetes, the provision and scaling of components is automated. For certain agent functions, serverless serverless architectures can also be used for certain agent functions, where computing resources are automatically provided as required. In principle, the architecture should horizontal scaling where performance is increased by adding further instances.

Adhering to these technical requirements is key to establishing business AI agents not only as innovative but also as secure, reliable, and future-proof enterprise solutions.

4.2 Governance and regulation of business AI agents

The rapid development of AI agents and their integration into business processes raises important questions about security, ethics and control. To reap the benefits of this technology while minimizing risks, the implementation of robust governance and compliance with regulatory frameworks is crucial. Parallel to the technological realization of AI agents, the technical possibilities for effective governance must be created.

The regulation of AI systems is a growing concern worldwide. The EU AI Act is a prominent example of this, as it takes a risk-based approach. High-risk systems, such as those used in critical sectors, are subject to stricter regulations. The goal is to strike a balance between innovation and security while creating uniform standards.

If an autonomous AI makes a mistake that leads to financial loss or damages the company's reputation, the question of responsibility must be clearly defined. It must be determined whether the developers, management, or operator can be held liable for the AI's actions. Clarifying these issues is fundamental for trust and legal certainty when dealing with AI systems.

AI agents must be designed in such a way that they comply with all relevant laws and regulations. A key example of this is the General Data Protection Regulation (GDPR), which places strict requirements on the processing of personal data.

The decisions and actions of an AI agent must be able to be logged. This auditability is essential for internal controls and serves to clarify responsibility in the event of damage. This concept is known as “Explainable AI“ (XAI) and aims to ensure the transparency and explainability of AI decisions. This is essential for the trust of users and stakeholders.

Technological requirements for AI governance

Effective AI governance requires a solid technological infrastructure that goes beyond pure model development.

Data and Metadata Management:

A clear, centralized data platform (e.g., a Data Lakehouse) is essential. Metadata management systems (Data Catalogs) enable the tracking of data origin, its transformation, and its use by AI agents. This forms the basis for auditability and reproducibility.

Automated monitoring and logging:

Continuous monitoring systems need to capture the AI agent's performance, drift, and fairness in real time. Comprehensive logging mechanisms record every action and decision, along with the inputs used.

Model registry and version control:

Each version of an AI model must be uniquely identifiable, storable, and reproducible if needed. A central model registry enables clear version management and tracking of model changes.

CI/CD Pipelines for ML (MLOps):

The use of Continuous Integration and Continuous Deployment (CI/CD) in ML development (MLOps) enables automated, responsible, and standardized deployment of AI models. This reduces manual errors and integrates governance steps into the development process.

Automated bias and fairness tests:

Technological tools must be integrated into the development process to automatically test models for potential bias and fairness before production. This contributes to compliance with ethical principles.

4.3 Risk management of Business AI agents

The introduction of autonomous business AI agents carries specific risks that must be proactively managed. An effective risk strategy is crucial to ensuring the security and stability of a company.

There are considerable security risks. An autonomous agent operating in the corporate network can be a gateway for cyberattacks if it is not adequately secured. Attackers could attempt to take control of the agent in order to access sensitive data or misuse it for malicious purposes. To prevent this, strict security mechanisms such as intrusion detection systems (IDS) must be implemented to monitor network traffic for suspicious activity. Regular security audits to check for vulnerabilities, data encryption and network segmentation are also crucial measures.

For an AI agent Emergency and termination procedures required. In critical situations, it must always be possible to regain control of the agent. There must be a clear way to stop the AI manually in an emergency or to correct its actions. A so-called "kill switch" is a necessary security measure that enables authorized persons to deactivate the agent immediately in the event of unforeseen or harmful behaviour. Emergency protocols must also clearly define who is authorized to intervene and what steps are to be taken in the event of a malfunction.

Finally, there is the risk of unintended consequences. An AI that is exclusively optimized to increase a single metric, such as sales, could have unintended negative effects on other areas of the company. A purely profit-maximizing AI could, for example, ignore customer satisfaction, use aggressive sales strategies or even damage the brand image. To avoid such negative consequences, the agent should be trained on not just one but several metrics that reflect the overarching business goals. A "human-in-the-loop" approachwhere human experts regularly review and correct the agent's behavior is also essential. In addition, ethical and business-related rules must be embedded directly into the agent's code to prevent undesirable actions from occurring in the first place.

5 Agentic AI: A paradigm shift for efficiency and productivity

Agentic AI represents a paradigm shift that has the potential to increase efficiency and productivity of companies radically increase. However, the development of business-agentic AI requires more than just technical expertise; it requires a comprehensive approach that takes into account security, ethical and governance issues issues from the outset. By proactively addressing these challenges, companies can reap the benefits of autonomous AI systems while minimizing the risks.

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Stay tuned for more wiki articles on data, analytics, and AI! And yes, they are all written by real nerds with passion.

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