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Whether you need to review invoices, process ERP orders, or analyze customer data, AI agents can assist with all these time-consuming business tasks.
This development is especially beneficial for small and mid-sized enterprises as AI agents make intelligent automation accessible even without in-house development teams or large IT budgets.
But how exactly does an AI agent differ from other systems? What is the underlying technological architecture? What opportunities do AI agents offer for ERP systems? And how can you successfully create an AI agent? You'll find the answers to these questions in this article.
What is an AI agent? – definition and basics
An AI agent is a software program based on artificial intelligence that can interact with its environment, collect data, and utilize it. In more complex systems, multiple specialized agents work together, coordinated by a master agent that manages the processes and integrates all results into a unified solution.
The concept of AI agents evolved from artificial intelligence research in the 1950s. Early systems could only perform simple rule-based actions, while today's AI agents can independently handle complex tasks and learn from their mistakes.
Unlike standard programs, they independently choose the best actions to achieve defined goals — a breakthrough for AI in the mid-market, as it eliminates the need for extensive programming and manual data transfers.
What types of AI agents are there?
AI agents can be classified by their learning behavior:
- Rule-based AI agents operate according to fixed, predefined rules and are ideal for repetitive tasks.
- Learning-based AI agents expand their knowledge through machine learning. They can consider past interactions and continuously improve their decisions as a result.
- Hybrid LLM agents combine rule-based decision logic with data-driven machine learning.
Another way to categorize agents is by how they interact with users: conversational agents engage in direct conversations and understand natural language in real time. Background agents work invisibly, automatically handling tasks without direct user interaction.
How does an AI agent work?
The functionality of AI agents varies depending on their type and architecture. Simple agents often rely solely on predefined rules. In contrast, advanced systems use complex learning algorithms and adapt to new situations.
The functionality becomes clear when looking at modern LLM-based systems: today's LLM-based agents operate through the interaction of several key components. Equipped with practical tools, these agents can understand and execute even advanced requests:
- The language model forms the core, capable of following instructions, drawing logical conclusions, and understanding complex queries.
- The orchestration layer, acting as the control center, coordinates all processes and manages memory and conversation histories.
- Tools for extended capabilities enable the agent to interact with external data sources, services, and functions beyond sole language processing.
How do AI agents differ from other systems?
AI agents differ from other systems by acting independently and proactively. In comparison, AI assistants respond to specific requests, generative AI creates content, and traditional AI systems support data analysis. Each system has its specific use case and varying levels of autonomy.
The key differences at a glance:
How do AI agents streamline processes in ERP systems?
AI agents enable smoother ERP processes through intelligent voice interaction within familiar work environments. They enhance collaboration by providing a unified user interface, delivering role-specific information, and automating processes.
Unified user interface
Mid-market enterprises often struggle with fragmented ERP landscapes that force employees to constantly switch between different modules and applications. AI agents solve this problem by providing centralized access points, for example, directly within Microsoft Teams or other employee communication tools. Instead of jumping between the accounting module, CRM, and inventory management, users can access all relevant information and functions through a single interface.
Role-specific intelligence
While traditional ERP dashboards overwhelm users with the same data views, AI agents deliver tailored insights based on each user's role and area of responsibility. For example, a sales manager receives proactive alerts about critical customer accounts, while a managing director is notified of budget deviations — all without having to manually sift through multiple reports.
Seamless collaboration through automation
Especially in growing small and mid-sized enterprises with distributed teams, AI agents simplify collaboration through AI automation by integrating ERP functions directly into existing communication channels. Approval processes, budget releases, and project updates are handled automatically through the familiar workflows. This means employees don't have to switch between different systems or wait for feedback.
How to create an AI agent: step-by-step guide
Phase 1: Planning and strategy development
- Define the use case: Identify specific logistical challenges such as inventory optimization or route planning.
- Evaluate the data landscape: Assess available information sources and their quality as a foundation for AI systems.
- Technology infrastructure: Choose scalable platforms for seamless integration into existing logistics systems.
Phase 2: Step-by-step implementation
- Develop tailored solutions: Focus on customized AI tools for specific tasks rather than imprecise all-in-one solutions.
- Start small: Start with routine tasks like automated document creation before tackling more complex processes.
- Connect systems: Integrate AI applications with warehouse management, transport planning, and other enterprise solutions.
Phase 3: Optimization and scaling
- Establish monitoring: Track performance metrics and user interactions for continuous improvement, and establish review processes to validate AI suggestions before making critical decisions.
- Expand to other areas: Systematically apply proven AI solutions in other areas to boost efficiency across the organization.
AI agents: limitations and opportunities
AI agents transform rigid automation into independently thinking systems — but this also introduces potential AI-related challenges. Intransparent algorithms can lead to biased decisions, create security vulnerabilities, and pose compliance risks.
Things become especially critical when AI agents are fed with faulty data. This inevitably leads to unreliable information and unusable results.
Successful implementation therefore requires clear processes: responsibilities must be defined, data quality ensured, and risk areas monitored. Documentation and regular reviews are essential.
The limitations and opportunities of AI implementation at a glance:
What does the future of AI agents look like?
AI agents are set to evolve from standalone tools into intelligent, connected systems that combine multimodal intelligence with local data processing, but the real breakthrough lies in intelligent agent swarms that will collaborate like specialized teams.
Rather than burdening a single AI with all tasks, networks with defined roles are emerging: while one AI agent evaluates customer emails, another generates personalized recommendations or searches the company knowledge base. This specialization boosts efficiency and ensures better results, with each agent concentrating on its area of expertise.
Cloud platforms are driving this technological evolution, and companies are increasingly embracing multi-cloud strategies to leverage the strengths offered by multiple providers.
Quellen:
Empolis (2024): Mehr Produktivität durch Generative AI, Ein Leitfaden für den sicheren und erfolgreichen Einsatz in der Industrie.
PTO: Industrial AI Platform pto alpha 2025. Produktbroschüre. Download unter: https://www.proalpha.com/de/loesungen/industrial-ai-platform/technologie-basis.
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