customer_support · saas · workflow

Alibaba Cloud Higress intelligent diagnostic system resolves over 95% of consulting issues and over 85% of anomalies using multi-agent AI

Building effective AI agents requires integrating multiple data sources, maintaining high-quality domain data, and ensuring reliable performance that meets customer SLA requirements.

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 data collection
Each application collects and consolidates personalized and specialized data from customers as the foundation for the agent.
Tools used
HigressSpring-AI-AlibabaNacosRocketMQOtelMicroservices Engine (MSE)DeepSeek · partnerRAG
Outcome

The intelligent diagnostic system built with Higress, Spring-AI-Alibaba, and MSE solves over 95% of consulting issues and over 85% of anomalies.

Results
Volumeover 95%
Source

https://www.alibabacloud.com/blog/development-trends-and-open-source-technology-practices-of-ai-agents_602037

How we source this →

Grounding & classification
Source type: technical build writeup
27 fields verified against source quotes.
agentic workflowconversational aimulti agent workflowragknowledge basebuilder submittedmetric backedproduction runtime claimedtools describedworkflow describedsoftwareautomation ratedeflection ratetechnical build writeupcustomer supportagentic task executionrag answering