Deutsche Telekom builds open-source LMOS multi-agent platform for customer-facing AI across Europe
Deutsche Telekom needed to deploy generative AI customer service automation across 10 European countries with different languages and multiple channels (chat, voice, autonomous), but no framework existed capable of scaling across this complexity without requiring deep Spring Boot expertise or tolerating brittle monolithic prompts.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Customer request via channel
A customer submits a request through the chat channel, voice channel, or an autonomous channel to the Frag Magenta system.
The LMOS platform has served over a million customer questions with an 89% acceptable answer rate, deflected more than 300,000 human-agent conversations at a risk rate under 2%, and outperformed vendor products on agent handovers by 38%. Domain agent development time fell from 2 months to 10 days.
What failed first
Early RAG chatbots on LangChain required deep Spring Boot expertise and heavy boilerplate; LLM prompts were fragile so any change risked breaking the entire chatbot; and no JVM-ecosystem multi-agent framework existed that could meet Deutsche Telekom's scalability requirements.
Results
Time savedfrom 2 months to 10 days
Volumemore than a million
Running sinceSeptember (year not specified in source)