Custom Development of AI Agent (Production-Grade Implementation with LangGraph / RAG)

Custom development of production-ready AI Agent applications for enterprises, delivering end-to-end services from requirement research, architecture design, development and implementation to evaluation and launch. I have led the production implementation of 2 enterprise-grade AI Agents: 1. AI Customer Service Agent: LangGraph orchestration + 10 atomic tools + entity slot memory, integrated with WeChat Work for production operation, supporting the customer service team’s self-service supply chain queries; 2. Financial Document Extraction Agent: LangGraph 19-node dual-path + VLM, full evaluation on 625 real documents / 2500 fields, with 97.0% field accuracy and P95 latency of 7 seconds. Deliverable Agent types: · Intelligent customer service / internal knowledge assistant (integrated with WeChat Work, DingTalk, Lark) · Document extraction / document processing Agent (VLM + structured output) · Business process automation Agent (multi-tool orchestration, approval workflow) · Internal Copilot (data query, report generation) Tech Stack: LangGraph / LangChain / ReAct / RAG (FAISS + BM25 hybrid retrieval) / Python · FastAPI / LangSmith observability / Guardrails safeguards / multi-model hierarchical scheduling. Deliverables include: fully functional code and deployment documentation, evaluation system (pre-launch data validation), operation and maintenance monitoring solutions. Remote collaboration is supported, with asynchronous communication as the main mode.

10 years of enterprise-level backend experience + 2 years of AI Agent production implementation (non-Demo level); Built-in evaluation system methodology, validate before launch, make decisions based on data; Experienced in enterprise system integration (WeChat Work / multiple business libraries), with knowledge of production environment fault tolerance, rollback and observability.