As an experienced company in the development of chatbots, the breakthrough of generative AI was a real game changer for us. The implementation of dialog-based AI applications has become much easier and therefore faster – the effort on the customer side has been significantly reduced. The flexible and personalized response behavior of AI chatbots increases the user experience and acceptance, allowing entire business processes to be optimized and redesigned. With Agentic AI, we are now on the cusp of the next evolutionary stage: the transition from dialog-based chatbots to action-oriented, autonomous chatbots. AI agents.
In this article you will learn:
- How AI chatbots can be developed into capable AI agents
- What technical requirements are necessary for this
- How companies sustainably manage the economic use of AI
- What to pay particular attention to when introducing and managing AI agents
From chatbot to digital assistant
At the latest with the triumph of ChatGPT, AI-supported chatbots have established themselves as a new, intuitive interface – both for external customer interactions and for internal applications. Initially, they facilitate access to information. In further expansion stages, they take on active roles in workflows, for example in:
- Appointments
- Complaints handling
- Product recommendations
- Onboarding processes
The next logical consequence is the linking of entire process chains: for example, leads can be automatically reported to sales teams, incomplete data records can be added to the CRM or individualized offers can be generated – all through the interaction of AI agents, API interfaces and integrated tooling. This new form of Work organization with intelligent, active AI components will fundamentally change the corporate world in the coming years.
Agent management: When AI instances grow
As the company’s AI expertise grows, the number of agents used in parallel also increases. A complex ecosystem of digital assistants quickly emerges that needs to be managed in a structured way. Platforms such as our aiSuite provide the necessary infrastructure:
- Processes can be modeled and transferred to agents
- Data sources and third-party tools can be connected centrally
- Active agents can be monitored in real time and flexibly adapted‘
The result is a controllable, scalable agent landscape that can be dynamically developed in line with the individual requirements of each company.
Technological requirements for AI agent systems
The building blocks for maximally automated processes have long been technologically available – but their integration varies considerably depending on the use case. Simple processes can be handled by a single agent. More complex requirements, on the other hand, benefit from a multi-agent approach in which specialized agents work closely together.
This is where orchestration comes into play: a central instance – such as a higher-level orchestrator agent – controls the distribution of tasks, monitors processes and coordinates decisions. This creates an intelligent, networked agent system that can operate stably and efficiently even when requirements grow.
Business impact: The economic use of AI agents
However, every automated request – whether from customers or employees – generates costs. The challenge therefore lies in the economic balance between performance, scalability and budget.
A technology-agnostic implementation in the aiStudio allows flexible access to different Large Language Models (LLMs) – depending on the requirements:
- Type of task
- Accuracy and relevance of the answer
- Availability of domain-specific knowledge
- Requirements for security and hosting factors
- Cost-benefit ratio
This modular architecture allows our customers to choose the optimum solution for every application – both technically and economically.
People as a success factor: a pragmatic recommendation for action
Despite all the technological advances, one thing is certain: the key to success lies in meaningful collaboration between man and machine.
Our customers regularly report positive transformation experiences. Employees are relieved, repetitive tasks are automated – and free space is created for creative, value-adding activities.
AI success definitely needs more than technology:
- Project managers must take over data maintenance, agent training and dialog analyses
- Internal initiators are needed to drive forward further developments
- The pace of change must match the corporate culture
In companies that follow this path, the development of AI expertise is increasingly seen as a strategic competitive advantage and acted upon accordingly.
