Advanced AI application demonstrating pragmatic automation of technical support workflows through sophisticated multi-agent architecture
Technologies Used
Multi-Agent System
Context Engineering
RPE
RAG
Human-in-the-Loop
Lean Prototyping
Streamlit
Python
Executive Summary
This comprehensive demonstration showcases how modern AI application principles can transform technical support operations for German SMEs. Using Pumpen GmbH, a fictional water pump manufacturer, as our case study, this project illustrates the practical implementation of multi-agent systems, context engineering, and human-in-the-loop design patterns to dramatically reduce human workload while maintaining service excellence. A fully interactive live demonstration is available, allowing direct interaction with the multi-agent system.
Business Context
Technical support represents a significant operational burden for manufacturing companies like Pumpen GmbH. Valuable engineering resources are diverted from core product development to handle routine customer inquiries about pump specifications, troubleshooting procedures, and maintenance protocols. This demo addresses a critical business challenge: how can advanced AI applications preserve technical expertise while automating repetitive support workflows?
Live Demonstration
Experience the multi-agent system in action through an interactive Streamlit application. The demo allows you to submit technical tickets and observe the coordinated response of multiple AI agents as they research, analyze, and formulate solutions using the RPE framework.
Source Code
Explore the complete source code implementation, including multi-agent architecture patterns, context engineering techniques, and integration strategies.
RPE Workflow Architecture
Like humans, artificial intelligences perform tasks better when they systematically engage with the task context before implementation. Humans don't jump directly into complex tasks, but first understand the task, research missing information and relevant references, then plan step-by-step actions before execution. The RPE (Research-Plan-Execute) framework mimics this systematic approach and greatly enhances agentic system capabilities. The diagram below illustrates this agentic workflow, showcasing both the RPE framework and its AI Agent Architecture working in harmony.
AI Agent Architecture
A best practice AI agent architecture should provide the AI with essential tools: context, akin to a small whiteboard for the AI to keep notes and relevant information. External resources for accessing relevant information with different types of access such as read or even write. Furthermore, AI agents benefit from having the ability of creating artifacts of their work - pieces of work that are worth having more static and long-term form than the ephemeral context and pieces of work that are relevant to the parties working with the AI. Such an architecture places an AI agent in the world with clear scope and boundary, making it easy for the AI agent to work effectively and for other parties to interact with it predictably.
Technical Approach
AI agent development, just as any other form of complex development, benefits immensely from lean agile development and prototyping to quickly and cheaply understand the context of the task and its difficult-to-foresee challenges. This demonstration was built using Streamlit, a library enabling rapid development of simple prototypes without focusing on UI complexity. The implementation is model-agnostic, highly modular, and deliberately avoids agentic orchestration tools such as LangChain or n8n. By taking this approach in early prototyping stages, one gains better understanding of task context and requirements for more complex, powerful tools. This strategy allows adaptation to sophisticated frameworks only once their value is proven worthwhile, avoiding premature technological lock-in.
Key Outcomes
This demonstration proves that sophisticated AI applications can deliver measurable business value without compromising service quality. The system showcases practical approaches to context preservation, agent coordination, and human-in-the-loop validation that are directly applicable to real-world enterprise implementations. The project establishes a framework for value-driven AI application design that prioritizes business outcomes over technological complexity.
Business Value
For German SMEs facing similar technical support challenges, this demonstration provides a roadmap for implementing advanced AI solutions that respect both technical requirements and business constraints. The approach emphasizes practical value generation, sustainable implementation practices, and maintainable system architecture that can evolve with changing business needs.