AI agents are intelligent software systems that can plan and carry out tasks largely independently. To do this, they identify a specified objective, process relevant sources and decide which individual steps are required.
Unlike traditional AI applications, agents therefore do not merely provide answers. They can also trigger actions, react to output and adapt their subsequent course of action accordingly.
An AI agent is usually based on an AI model that is linked to other components. These include, for example:
This enables it to handle even complex workflows consisting of several interdependent steps.
The specific benefits of agents therefore depend crucially on the conditions within the organisation. An agent can only operate reliably if
Incomplete data sources result in the AI agent preparing incorrect actions, being unable to complete tasks or continuing to require a great deal of manual intervention.
AI agents work in a goal-oriented manner: they are given a task, determine the necessary steps and carry them out independently.
The specific way in which they operate can be visualised as follows:
Depending on the specific application, several agents may work together: for example, one agent analyses a query, another checks stock levels or delivery dates, and a third prepares a response.
Such multi-agent systems tackle complex tasks by dividing the work amongst themselves. To this end, responsibilities, handover procedures and decision-making rules must be clearly defined to prevent contradictory outputs or erroneous actions.
Chatbots and AI agents differ primarily in terms of their purpose and their degree of autonomy:
In practical terms, this means that chatbots are primarily designed for communicating with people. They answer questions, provide information or guide users through a dialogue. Their purpose usually ends with a response or a recommendation. An AI agent, on the other hand, can also independently handle tasks, prepare decisions and carry out actions.
This also distinguishes the degree of autonomy. A chatbot usually responds to a specific input and then waits for the next question. An AI agent, on the other hand, pursues a defined objective, takes interim results into account and initiates further steps within its scope of action. In doing so, the agent has access to datasets, applications and digital tools.
However, the boundary between the two technologies is fluid. A chat can serve as a user interface for an agent: for example, staff members formulate their enquiry in the chat whilst the agent processes the request in the background. What is decisive, therefore, is not the visible interface, but the underlying functionality.
Companies generally use AI agents to handle repetitive or time-consuming tasks more efficiently. These include, for example:
The greatest benefit often stems not from a single agent, but from its integration into end-to-end business processes. This relieves staff of administrative tasks in particular, allowing them to focus more on strategic tasks.
Specific examples of how agents can be used include the following:
Customer Service
In customer service, agents can categorise enquiries, compile reports and develop appropriate solutions. Complex cases can be automatically forwarded to the relevant staff members.
Purchasing, Logistics and Production
In this area, an AI agent can, for example, check demand reports, monitor delivery dates, identify discrepancies or suggest courses of action in the event of delays. In this way, agents help staff to react more quickly to changes and manage operational processes more efficiently.
Finance, Human Resources and Sales
Possible tasks for agents include, for example, checking documents, analysing key performance indicators, preparing quotations or prioritising sales opportunities. Use cases where large volumes of data and information need to be evaluated and processed according to clear rules are particularly relevant here.
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AI agents can help businesses increase efficiency, automate processes and reduce errors.
Depending on the area of application, the benefits of agents include:
Companies benefit particularly greatly when they integrate agents specifically into relevant end-to-end processes, rather than simply automating individual tasks in isolation.
Risks generally arise when an AI agent is granted overly broad permissions or draws on faulty sources.
The effectiveness of agents is closely linked to their data sources. If they receive incorrect, out-of-date or contradictory information, they will consequently produce erroneous results.
However, as an AI agent not only provides answers but can also trigger actions, these errors may have a direct impact on business processes. Particularly when dealing with complex tasks, it must therefore be possible to trace which data sets were used by the agent and how a decision was reached.
Further risks relate to data protection, information security and access rights.
If an AI agent is granted overly broad permissions, it could process confidential data, make unauthorised changes or circumvent security protocols. Companies should therefore specify precisely which data sets and systems agents are permitted to access, which actions require approval, and when responsibility is handed over to a human.
Furthermore, unclear lines of responsibility and inadequate oversight can lead to poor decision-making or breaches of compliance.
An AI agent should therefore not be allowed to act entirely independently, but should be deployed within clearly defined rules and responsibilities. Regular audits, documented decision-making processes and human oversight mechanisms help to identify risks at an early stage and ensure the system is used safely.
To ensure that AI agents operate reliably, organisations must establish a suitable organisational and technological foundation. This includes, in particular, a structured database, seamless end-to-end processes, a modern ERP landscape and a clear AI strategy.
Structured data foundation
Agents require a reliable data foundation to identify correlations, prepare decisions and execute tasks correctly. If master data is incomplete, data sets are out of date or data formats are inconsistent, the risk of erroneous results increases.
Users should therefore define clear responsibilities for data records, establish quality standards and make relevant sources available across all systems.
End-to-end processes
An AI agent can only act effectively if the underlying business process is designed to be transparent and unambiguous. Organisations need to understand how individual process steps are linked, which rules apply, and at which points approvals are required.
End-to-end processes should therefore be analysed, standardised and designed, where possible, without any breaks in the workflow. Solutions such as SAP Signavio can help to visualise process flows and identify suitable areas of application for SAP AI agents.
Modern ERP landscape
An AI agent must have access to an up-to-date database and be able to carry out actions in the relevant applications. To achieve this, companies need an integrated system landscape with clearly defined interfaces.
A modern ERP platform such as SAP S/4HANA can provide a central foundation by linking processes and business data. The SAP Cloud is generally the ideal operating model for providing a suitable, future-proof system landscape. Building on this, SAP Business AI and SAP Joule can be integrated into the respective business context. Highly fragmented legacy systems and isolated data sets, on the other hand, make enterprise-wide use more difficult.
A Clear AI Strategy
Before SAP customers introduce agents, they should define specific objectives, prioritised use cases and measurable success criteria. Not every task that can be technically automated also offers sufficient economic benefit.
A clear AI strategy therefore defines where agents are deployed, what scope for action they are given, and how people remain involved in decision-making. It also takes into account responsibilities, data protection, information security, compliance and the necessary acceptance amongst staff. In this way, the use of agents does not become an isolated technology project, but rather a targeted component of digital transformation.
In the SAP context, agents act as intelligent process assistants that link business data, applications and workflows, and provide automated support for operational tasks.
As part of SAP Business AI, the agents are designed for specific business use cases. The so-called Joule Agents possess business process knowledge and can handle workflows across various corporate functions, whilst SAP Joule provides centralised access to the agents.
The particular added value of SAP AI agents lies in their business context. They can take input from connected applications into account, recognise dependencies between data sets and processes, and carry out appropriate actions based on this.
This makes them suitable, for example, for complex tasks in procurement, finance, human resources, the supply chain or customer service, which involve multiple process steps and systems. Joule Agents can also work together in a coordinated manner to handle cross-functional processes.
In this way, agents contribute directly to SAP’s vision of the Autonomous Enterprise: an AI-native enterprise architecture in which applications, data sets and AI logic are closely integrated, and business processes are increasingly supported, coordinated and executed automatically within clear governance structures.
Agents thus expand the SAP system landscape to include an intelligent level of action, but do not replace the necessary process and data foundation. With SAP S/4HANA as the central ERP platform, the SAP Cloud as the operating model, and SAP Signavio for the analysis and further development of end-to-end processes, SAP users can create the appropriate conditions for the deployment of agents.
AI agents represent the next step in the practical application of artificial intelligence: they not only answer questions, but can also plan tasks, evaluate sources, utilise systems and act independently within clearly defined limits.
For businesses, the real added value of agents lies in their integration into relevant business processes, where they specifically relieve staff of recurring, data-intensive or time-critical tasks.
At the same time, the success of agents depends less on individual AI functions than on a company’s organisational and technological maturity.
Structured data sets, reliable end-to-end processes, modern ERP systems and clear governance rules are crucial to ensuring that agent-based AI can be deployed securely, transparently and cost-effectively – in the SAP context, particularly with regard to SAP Business AI, Joule Agents and the vision of the Autonomous Enterprise.
