Agentic AI
From Automated Workflows to Orchestrated Multi-Agent Systems
Agentic AI: The Next Level of Intelligent Process Automation
Most processes do not consist of individual tasks, but rather of decisions, exceptions, and approvals. Traditional automation covers what is stable and rule-based. As soon as variations and unstructured information come into play, it reaches its limits.
This is where Agentic AI comes in. AI agents use context, interpret information, access systems via defined tools, and act within established guidelines. What matters most is not maximum autonomy, but the right automation approach for each task.
Why It's Worth Taking a Look at Agentic AI Right Now
Traditional automation is reaching its limits
Variations, exceptions, and incomplete information cannot be meaningfully represented using rules.
Unstructured data becomes usable
AI agents interpret emails, PDFs, contracts, and tickets, rather than just reading fields.
Cost Pressure Amid Scarce Resources
AI agents provide targeted relief at bottlenecks.
Lead time is becoming a competitive factor
Follow-up questions and rework decrease when clarifications are handled automatically as part of the process.
The technology is ready for deployment
Orchestration, tool integration, and open standards make productive use possible—not just pilot projects.
Two Ways to Implement Agentic AI
Not every process requires the same degree of autonomy:
- Stable, rule-based processes work best as workflows.
- AI agents help when there is uncertainty and a decision needs to be made.
- A multi-agent system is only worthwhile when multiple roles, systems, or subtasks need to be coordinated.
This results in two implementation approaches, which we combine where appropriate.
Workflow Automation: Get Up and Running Quickly on Proven Platforms
Where processes can be clearly defined and run frequently, we enhance established platforms with agent-based capabilities. First, Document Understanding extracts the relevant content from documents. An agent then evaluates the context and decides on the next steps before the workflow executes the corresponding actions. RPA continues to be used in cases where systems do not offer an API. In this way, we specifically enhance existing automations rather than replacing them.
Multi-Agents: Custom-Developed for Your Processes
When a process chain involves multiple systems, business logic, and areas of responsibility, we develop an agent-based architecture for your data and system landscape. Specialized AI agents handle clearly defined subtasks. An orchestrator agent coordinates handoffs and results and escalates to a human when necessary.
- Clear roles: one task per agent, individual permissions, individual access to tools
- Integration with your systems: ERP, CRM, DMS, and data platforms; RPA where no interface exists
- Evaluation & Monitoring: ongoing assessment of agent behavior, quality of results, and costs
- Interoperability: open standards such as MCP, no license lock-in
Using Agentic AI in a Controlled Manner
As soon as an AI agent accesses systems or performs actions, it becomes part of the business process. We determine the extent to which it is allowed to make its own decisions for each task.
- Traceability: Decisions based on evidence and with the option for human intervention; human-in-the-loop for critical steps
- Identity & Permissions: unique identity for each agent, access limited to what is necessary (least privilege)
Agentic AI in Practice
But how exactly do agents impact my processes and my company? In the following three use cases, we’ll briefly show you what’s now possible with agents.
Agentic Workflow Automation
- Current situation: Interpreting needs, requesting and comparing quotes via email, and going through multiple approval cycles. Purchase requests require a significant amount of manual effort.
- With Agentic AI: A purchasing agent consolidates requirements and generates the account-assigned purchase requisition in SAP and manages the request for proposals and evaluation. It clarifies any questions in advance, but final approval remains with a human.
Multi-Agent System
- Initial Situation: Requests come in via email, the portal, and the phone, and are passed back and forth between teams. Each handoff takes time.
- With Agentic AI: Specialized agents classify the inquiry, verify order and master data, implement the change, and draft the response. The Orchestrator agent coordinates the process and escalates issues when there is uncertainty.
Initial Situation
Contracts are a central component of our business world. In many companies, they are reviewed and managed manually: content and deadlines must be entered, monitored, and tracked by hand. As a result, termination or renewal deadlines are often identified too late, and deviations from standard terms frequently go undetected. This can lead to risks that can only be managed through significant manual effort. Added to this are late fees or consequences for day-to-day business operations—for example, when licenses and, consequently, critical processes fail because they were not renewed in a timely manner.
With Agentic AI
A digital agent automatically analyzes contracts, identifies relevant clauses such as terms or notice periods, and continuously monitors them—even in collaboration with humans. The agent initiates renewals or renegotiations in a timely manner and identifies deviations from the company’s own defined standards. Secure, transparent, and verifiable at any time. This not only saves money but also saves you from worry.
Our Services: From the Initial Consultation to Stable Operations
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Initial Consultation & Inspiration
During an initial one-hour consultation, we’ll assess your process. To provide a broader perspective, we offer Digital Inspiration Days, use-case presentations, and collaborative ideation workshops.
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Assessment
We evaluate use cases based on business value, agentic fit, feasibility, and risk & governance, and prioritize them according to benefit and feasibility.
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Pilot
A clearly defined, production-oriented pilot validates the benefits, process design, and degree of autonomy. The scope and level of autonomy increase only once the effectiveness has been demonstrated.
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Implementation & Integration
We use Agentic Workflow Automation on UiPath, n8n, or Microsoft Power Platform and develop custom multi-agent systems. We migrate existing automations, for example from Blue Prism to UiPath. Integration is handled via APIs, RPA, and open standards such as MCP.
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Confident Operation
Managed AI Services with monitoring and continuous development. Operated on-premises, in a private cloud, or in a shared environment, all in certified German data centers.
Why Arvato Systems?
We support you with a clear end-to-end approach throughout your data and AI journey: from strategic planning and building the right data foundation to successful implementation and secure operation.
You start where the greatest value is created and use that as a foundation to develop scalable data and AI solutions that deliver measurable business success.
One Partner. From Data to AI.
Our Partners
Frequently Asked Questions About Agentic AI
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What is the difference between Agentic AI and Agentic Automation?
Agentic AI is the umbrella term for systems that pursue goals, interpret context, and use tools. Agentic Automation refers to their application in business processes, often building on existing RPA and workflow environments.
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Platform or custom development—which is right for us?
High-volume, near-standard processes run faster and more cost-effectively on a platform-based approach. Processes that are unique to your business model are more likely to warrant a dedicated agent architecture. In the assessment, we evaluate this on a per-use-case basis.
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When do I need a multi-agent system—and when is an AI agent enough?
A single AI agent is suitable for a clearly defined task with a manageable context. A multi-agent system becomes relevant when multiple roles, systems, or subtasks need to be coordinated, or when results need to be verified independently. Typical architectural patterns include:
- Supervisor–Worker
- decentralized coordination
- Generator-Critic
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How long does a pilot project last?
A clearly defined, production-oriented pilot can usually be implemented within a few weeks. We expand its scope and autonomy only after obtaining reliable results.
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What operating models does Arvato Systems offer?
From supporting your own team to comprehensive managed AI services. Operations can be conducted on-premises, in a dedicated virtual private cloud, or in a shared environment—all in certified German data centers.
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