Multi-Agent Systems: Practical Examples from German Mid-Sized Businesses

Multi-Agent systems are where individual AI agents, cost calculator: AI development, digitalization in mid-sized businesses and IT- & digital consulting.

A single AI agent with a very wide range of tasks suffers from the same problems as a human generalist, who should do everything at the same time.

Quality losses due to change of context, errors due to too many parallel needs, difficult to understand decisions.

• Multi-Agent systems solve this by: Specialization:** Each agent is improved for a clearly defined partial process – with matching prompt, matching tools and matching output format.

Parallelization: Separate subtasks are processed simultaneously, not sequential. What an agent does in 10 minutes makes three expert agents parallel in 4 minutes.

**A Critic agent checks the issue of other agents for errors, inconsistencies or quality defects before the result is passed on.

Modularity: Custom agents can be replaced, improved or replaced by other systems without rebuilding the entire system.

Architecture Patterns for Multi-Agent Systems

Orchestrator Worker Pattern: A central orchestrator agency takes up the task, disassembles it in partial tasks and delegates it to expert worker agencies.

Workers report back, the Orchestrator aggregates and outputs the overall result. This pattern is suitable for well structured processes with clear substeps.

• Pipeline pattern: Agents are arranged in a chain. Each agent processes the output of the previous one and forwards its output to the next. Well suited for processes where each step builds on the previous – e.g. document extraction → validation → enrichment → booking.

Hierarchical pattern: Several orchestrators coordinate their own worker teams. A senior coordinator controls the orchestra gates. For very complex processes with several separate process strands.

Debate/Critic pattern: Several agents generate solutions or reviews independently of one another. A Critic agent or voting mechanism selects the best result or synthesizes the inputs.

Increases quality at the expense of latency and costs.

Practice Examples: What works in mid-sized businesses today

**Example 1: Automated supply creation in mechanical engineering **

A midsize mechanical engineer received 15–30 requests per day for custom special machines.

Each request required: technical feasibility review, material price determination, capacity check, costing, supply creation.

• Setting up the multi-agent system: Intake agent: Liest and structured the incoming request.
• Technical review agent: Checks feasibility against component database.
• Price agent: Calls current material prices via ERP interface.
• Capacity Agent: Checks production calendar and available slots.
• Calkulation agent: Calculates offer by price structure rules.
• Quality check agent: Controls output for completeness and plausibility.
• Offer Agent: Creates formatted quote document.

Result: 80% of standard requests are processed fully automatically, processing time is reduced from 4 hours to 12 minutes.

Complex special cases are handed over to sales staff with a completely prepared context.

**Example 2: supplier monitoring in purchasing **

Every day, a supplier for the automotive industry had to monitor dozens of suppliers in terms of delivery, quality reports and price changes.

Construction:

• Monitor agents (parallel): Monitor supplier portals, industry newsfeeds, quality databases.
• Review Agent: Rated Significance of new information for your own purchase.
• Risk agent: Rated supplier risks according to defined criteria Report agent: Creates daily morning report for purchasing management.

Result: purchasing management gets a structured risk report every day instead of monitoring 40+ sources. Early warning signals are detected 2–3 days earlier.

**Example 3: Compliance Testing in Financial Services ** A financial service provider checked incoming contract documents manually for completeness and compliance – with considerable time.

Construction:

• Documents Extraction Agent: Read contract documents, extracts relevant fields.

• Fullness Agent:* Checks against checklist of mandatory documents.

• Compliance Agent: Checks extracted clauses against regulatory needs.
• Risk Assessment Agent: Rated total risk profile according to internal scoring rules.
• Escalation agent: Decides routing: auto-release, specialist, compliance department.

Result: 60% of cases are processed autonomously. Specialists only process border cases and exceptions – with fully prepared information.

Critical success factors

** Clear process documentation before automation.** Multi-Agent systems can only automate what is clearly recorded.

Processes that live "in the head of the specialist" must first be made explicit.

** Clean interfaces to existing systems.** The value is created by integration. Good API development and system integration is a prerequisite, no incident.

Solid against errors and exceptions. Real processes have exceptions. The system must deal with it in a defined way: fallback on human processing, logging, alarming.

Stepwise structure. No multi-agent system is created completely from the beginning. Start with the simplest value-creating sub-process, then stepwise extension.

Monitoring and quality assurance. Regular sampling of agent decisions, quality metrics and drift detection (if the model becomes worse because input data change).

Integration with existing IT landscape

Multi-Agent systems are not a substitute for ERP, CRM or other core systems – they are the automation layer above. Integration is over:

• REST APIs for modern systems.
• RPA bridges for legacy systems without API.
• ** Database access** for direct system integration.
• Message queues for asynchronous, scalable processing.

Our team at Groenewold IT Solutions has experience of integration into typical midsize systems – from SAP and Navision to industry-specific own developments.

Conclusion: Where multi-agent systems really make sense

Multi-Agent systems are powerful – and correspondingly complex to build and operate. If:

• Yes. The process is complex enough to justify specialization.
• Yes. The volume is high enough to amortize the development effort.
• Yes. The quality needs are high enough to make Critic agents meaningful Anyone who stands at the beginning should start with a single well-integrated AI agents – and consider multi-agency architecture as the next stage of development if the pilot was successful.

Frequently Asked Questions (FAQ)

How much does a multi-agent system cost in development?

Depending on the complexity and complexity of integration. Simple pipeline systems (3–4 agents, 2 system integrations) from about €40,000–80.000.

Complex orchestrator systems with many integrations and high quality needs correspondingly more.

What frameworks are used for multi-agency systems?

LangGraph (for complex, conditional agent workflows), CrewAI (for role-based agent teams), AutoGen (Microsoft, for agent collaboration), LlamaIndex for knowledge-based applications. The choice depends on the application.

Can multi-agent systems be operated on-premise?

Yes, with local LLMs (Llama 3, Mistral, Qwen). Quality is the price for sovereignty in data protection-critical applications – current local models are good.

However, still behind GPT-4o class.

How long does the construction of a multi-agent system take?

Realistic: 3–6 months for a fully integrated, production-ready agent workflow. Faster results with limited scope or demo quality are available in weeks.

Technical sources and further links

The following separate references complement the grouping on the topics of this Article:

• Bitkom – Digital Economy Association.
• BSI – Federal Office for Information Security.
• European Commission – Digital Strategy.
• MDN Web Docs (Mozilla)
• W3C – World Wide Web Consortium.

"ERP projects rarely fail at the software list, but at unclear process boundaries and lack of expertise in the project."

— *Björn Groenewold, Managing Director, Groenewold IT Solutions *