customer_support · healthcare · workflow
Doctolib builds a multi-agent agentic RAG architecture for customer care
Doctolib's customer care relied on RAG with LLMs but hit limits requiring more powerful solutions, including handling complex multi-step tasks, accurately routing user queries, and making sense of unstructured data.
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 · User submits natural language query
The agentic system receives a customer care request from a user expressed in natural language.
Tools used
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Outcome
Doctolib produced a working POC of a multi-agent agentic RAG system for customer care, demonstrating correct routing, context fetching, and user-validated execution of a practitioner's agenda access request, though production deployment remains a challenge ahead.
What failed first
The agentic system faced challenges including non-deterministic agent behavior causing incorrect tool invocations, oversized prompts degrading instruction-following, and difficulty extracting meaning from unstructured data consistently.
Grounding & classification
Source type: technical build writeup
21 fields verified against source quotes.
agentic workflowconversational aimulti agent workflowragknowledge basefailure mode describedhuman review describednamed customertools describedworkflow describedhealthcaretechnical build writeupcustomer supportagentic task executionescalation workflowrag answering