AI agents for companies: Practical guide for a successful start

The integration of AI agents into everyday corporate life is becoming the central trend of 2025. This comprehensive practical guide shows how companies of all sizes can create and implement their own AI agents in simple steps to optimize workflows and relieve employees. The article is based on current studies and expert assessments and offers concrete instructions for identifying suitable use cases, effective prompt design and implementation of AI agents in existing company processes.

Why AI agents are becoming a game changer

AI agents are more than just simple chatbots. They represent the next evolutionary stage of artificial intelligence. An AI agent is an “autonomous software entity or system that uses artificial intelligence and machine learning to perform specific tasks, make decisions and interact in complex environments”. In contrast to conventional chatbots, AI agents can act independently, initiate processes and handle complex tasks autonomously.

Market researchers at Gartner have named AI agents the “Top Technology Trend 2025” and predict that they will “make one in six to one in seven decisions autonomously” by 2028. Gene Alvarez, Vice President and Analyst at Gartner, even predicts that “by 2028, at least 15 percent of daily work decisions will be made autonomously by agent-based artificial intelligence – compared to zero percent in 2024”.

This development will fundamentally change the way we work. Michael Wallner, Head of Generative AI GTM EMEA Central at Servicenow, emphasizes: “The potential uses of AI agents are almost unlimited. They can answer customer queries in real time, forward complex queries to employees and create customized offers”.

Challenges that AI agents address

Modern companies face numerous challenges that make the use of AI agents particularly relevant:

• Information overload and system complexity: the volume of software solutions is leading to an increasing lack of transparency within the company, making it increasingly difficult to access information quickly and easily.
• Increasing time pressure: At the same time, ever faster growing amounts of data and greater time pressure make it impossible for employees to deal with the company’s information and data in sufficient depth and for long enough.
• Demographic change and loss of knowledge: Over the next few years, many knowledge carriers [will] take with them knowledge that has often been built up over decades – what remains for the next generation is the digital footprint.
• Falling traffic figures due to AI chatbots: For companies with a strong online presence, there is a further challenge: as predicted by market researchers at Gartner, the search engine volume is set to fall by 25 percent by 2026 as users increasingly use AI chatbots instead of search engines.

How to Find Suitable AI Use Cases in Your Company

The first step towards the successful implementation of AI agents is identifying meaningful use cases. The principle here is: “Think of AI simply – if AI can support a particular step (e.g., when composing an email, a white paper, or explaining a technical detail), that is already a first success.”

A structured approach to identifying suitable use cases includes:

1. Analysis of your own activities: Which recurring tasks take up a lot of time?
2. Evaluation of document and text work: Where are information from various sources regularly combined?
3. Identification of communication processes: Which standardized communication processes could be automated?

Particularly promising entry-level use cases include:

• Customer Service Agent: Answers customer inquiries around the clock, accesses the complete order history, and can handle returns directly.
• Recruitment Agent: Screens incoming applications, conducts initial interview rounds via chat, and organizes job interviews.
• Data Analysis Agent: Continuously monitors sales figures, detects trends and anomalies, and generates reports automatically.

In our white paper, we have written down some concrete prompts to help users identify use cases based on their own jobs and create the right prompts for AI agents.

The following is included in the white paper:

• A prompt on how to find suitable use cases based on your job and tasks
• A prompt on how to develop your ideas for prompts based on a few bullet points to consistently achieve better results
• A prompt on how to work with the AI to create advanced, personalized instructions for sustainably good results with your agents
• Ideas for various use cases that have proven successful in practice

Sounds interesting? Then download our white paper now:

Effective Prompt Design: The Key to Success

The core of every AI agent is its prompt design. A well-structured prompt includes the following elements:

• Role: Defines from which perspective the agent should act.
• Context: Provides relevant background information.
• Target Audience: Describes for whom the output is intended.
• Format Requirements: Specifies how the response should be structured.
• Task: Defines precisely what the agent should do.

For consistently good results, the use of a “System Prompt Engineer” is recommended – a special AI agent that helps create optimal prompts.

Step by Step: Creating Your First AI Agent

The practical implementation of an AI agent typically involves the following steps:

• Naming and Description: Give the agent a clear name and a precise description of its function.
• Formulate Individual Instructions: Define the use case and provide clear instructions.
• Select Knowledge Base: Choose relevant information sources for your agent.
• Select AI Model: Decide which AI model is best suited for your use case.
• Set Access Rights: Determine who may use the agent.

A concrete prompt is linked in the white paper above. You can start directly building your agents with it.

Advanced Prompting Techniques for Experts

For even more precise results, several advanced prompting techniques are available. A full list of various prompting techniques is also included in the white paper:

Role-Based Prompting

In role-based prompting, you assign the AI model a specific role, such as “expert”, “customer”, or “teacher”. This allows tasks to be handled from a specific perspective, which is especially useful in sales, marketing, or simulating customer situations.

Context-Based Prompting

Here, you provide the model with relevant background information before the actual question. The more context the AI receives, the more accurate the responses will be – especially valuable for complex tasks.

Chain of Thought Prompting

This technique asks the model to explicitly display its thought process before providing a final answer. This breaks down complex tasks into understandable steps, improving accuracy in logical or multi-step questions.

Self Consistency Prompting

In this method, you have the model generate multiple response suggestions and then choose the best or most consistent solution. This reduces the risk of incorrect answers and improves the quality of results.

Practical Examples: AI Agents in Corporate Use

Numerous companies are already successfully using AI agents:

• eBay uses AI and machine learning to improve the user experience with personalized product recommendations.
• CNN uses AI in content creation to identify relevant topics and tailor content.
• Netflix implements AI agents for personalized recommendations to increase site traffic.
• Airbnb relies on AI-driven algorithms for pricing its accommodations, improving customer satisfaction.

Outlook: AI Agents as a Key Future Technology

AI agents will be increasingly integrated into business processes in the coming years. However, it is important that they are meaningfully integrated into existing business processes and have access to the relevant know-how. If AI agents make decisions without the appropriate knowledge, they will make faulty decisions, and the added value will not be as great as expected. Solutions like amberSearch, however, assist with access to the right data sources. We already see how quickly our customers apply our technology at amberSearch and achieve value and efficiency gains from day one.

Want to learn more about amberSearch? Then contact us now via our contact form: