Artificial intelligence (AI) has made impressive progress in recent years. Developments in the field of AI assistants and AI agents are particularly exciting. But what are the differences between these two technologies? In this article, we explain the key differences and highlight how they work together to help companies optimize workflows.
AI assistants: smart helpers at a glance
AI assistants are applications that can usually perform tasks for a user with the help of a chat function. They require defined prompts to become active and are also able to use other tools to a limited extent after suitable training.
It is possible to tailor AI assistants to the needs of a user, but they do not necessarily have a permanent memory – this means that they cannot learn from previous interactions or improve independently over time. The assistants can only be improved when the manufacturer develops and releases a new version.
Function:
While the first AI assistants used to rely on rule-based instructions, predefined answers and tasks, nowadays they rely almost exclusively on machine learning (ML) or large language models.
Basic models such as IBM Granite or Meta’s Llama models specialize in text-based tasks. They enable the assistants to understand queries and then provide information and suggestions on how to proceed. AI assistants can also communicate with users via chatbot interfaces. An example of this is IBM’s watsonx Assistant or ChatGPT.
Use cases:
AI assistants are particularly suitable for performing manual tasks, retrieving specific information, analyzing data and recognizing trends and patterns, and creating content. They rely on human logic and are therefore used in the following applications in particular:
Customer service: Support customers by providing quick assistance and referrals, analyzing requests for trends and sentiment, and simulating interactions for training and product testing.
Database query: Support for querying and analyzing data. Users can ask questions in natural language without any prior technical knowledge, which are automatically converted into precise database queries. Quick access to relevant information and the creation of complex reports. You can find an example of the application of this technology on our website here.
Digital work: Automation with AI optimizes processes in almost all areas – from HR and contract tasks such as job descriptions, CV sorting and document analysis to customer service, marketing and data analysis. This leaves more time for strategic and creative activities.
Code generation: Support for developers by creating code from text instructions or optimizing code, with security ensured by expert review.
Virtual assistant: Performing preset tasks on command, such as reading out the latest emails or automatically providing a daily summary of news, appointments and weather. Could become even more powerful in the future through the integration of generative AI, as in Apple’s plans for Siri.

AI agents: autonomous problem solvers for complex tasks
In contrast to AI assistants, AI agents are systems or programs that develop workflows independently on behalf of users.
They also have the option of using external data sets and tools, such as social media APIs or Google, in order to make more informed arguments based on a broader database, make better decisions and solve problems more efficiently. Agents can also independently evaluate and decide which tools are best suited to the task at hand.
In this context, a flexible framework means that the AI agent is not limited to a specific environment or rigid specifications. Instead, it has the ability to adapt to different situations, tasks or platforms.
Function:
In order to become active, AI agents initially need a starting impulse that provides them with the basis for independent work. By using tools and databases, the agents can then evaluate and divide up an assigned goal and tasks in order to develop their own strategy.
AI agents are not limited to specific environments or rigid constraints, allowing them to go beyond the familiar chat environment and act in a more versatile way. They are able to reflect internally and make proactive decisions, which supports their problem-solving ability and continuous learning. They also store previous actions, conversations and experiences to learn from for the future and continuously improve their performance.
Use cases:
Due to their strengths in strategy development, autonomous action and learning, AI agents are more suitable for tasks with reasoning and support capabilities. The most common use cases therefore include:
Personnel management: Automated screening of applications through semantic analysis, optimization of employee productivity through analysis of work patterns and suggestions for training or task distribution.
Automated trading: Analysis of trends and news to predict market behavior and make lightning-fast trading decisions.
Network monitoring: Real-time network monitoring, threat detection and support for the IT team through process automation.
Customer service: chatbots and virtual assistants for fast customer service and round-the-clock support.
Financial and risk analysis: Automated review and analysis of financial data for fraud detection, as well as optimization of tax and spending strategies through data analysis and simulations.
Collaboration between AI agents and assistants: synergies for maximum efficiency
Generative AI opens up new possibilities for optimizing work processes, accelerates routine tasks and increases productivity. The added value does not come from direct collaboration between these technologies, but from their targeted use in the areas where they work best.
While AI agents handle tasks efficiently in the background, AI assistants ensure that the results and processes remain easy to understand and accessible. This coordinated use leads to improved overall efficiency and a more intuitive user experience.
Both technologies are based on state-of-the-art machine learning and natural language processing (NLP) approaches. Advances in these areas ensure that the respective tasks are performed quickly and accurately, without the specific strengths of one technology being replaced by the other.

Conclusion: AI assistants and agents working together perfectly
To summarize, AI assistants provide reactive support for clearly defined tasks, while AI agents work autonomously, use external data and develop independently. Together, they offer an ideal combination of user-friendliness and strategic depth, enabling comprehensive and efficient support for companies.
Want to know how you can use AI for your company? Contact us – we will be happy to help you.