name	enterprise-knowledge-assistant
description	Company-internal assistant for HR policy, product pricing, technical API docs, and order/inventory/notification workflows. Use deterministic guards for ORD/SKU/notification requests before routing between RAG, MCP tools, and direct answers.
allowed-tools	["mcp__enterprise-demo__query_order","mcp__enterprise-demo__check_inventory","mcp__enterprise-demo__send_notification"]
model	claude-3-5-sonnet
version	1.0

Enterprise Knowledge Assistant Skill

This is the Day 5 capstone packaged as a reusable Skill. It combines RAG, deterministic route guards, MCP tool calls, review, and evaluation. The teaching point is not "write a bigger prompt"; it is defining clear boundaries and verifying them with eval cases.

Architecture

User question
  -> Route guards + PlannerAgent
  -> one of:
       - Agentic RAG for HR/product/API knowledge
       - MCP tool call for orders, inventory, notifications
       - Direct answer or safe refusal
  -> ReviewerAgent quality check
  -> Final answer

All major steps are wrapped with Langfuse-compatible @observe spans. Full implementation notes are in reference/architecture.md.

When To Use

"入职 7 年有几天年假？"
"API 限流是多少？"
"StarLink 企业版多少钱？"
"查订单 ORD-002"
"SKU-A100 库存还有多少？"

The skill should refuse or defer when the knowledge base does not cover the topic, rather than guessing.

Programmatic Entry Point

from pipeline import upgraded_pipeline

result = upgraded_pipeline("入职 7 年有几天年假？")
print(result["path"])
print(result["answer"])

Return shape:

{
    "path": "rag" | "mcp" | "direct",
    "answer": str,
    "review": str,
}

Evaluation

eval.py provides batch_eval(dataset_path) and reference/eval_cases.jsonl contains the default teaching eval set. The eval is intentionally small; its purpose is to catch routing and grounding regressions during the course.

Configuration

DASHSCOPE_API_KEY=           # fill with your own key
LLM_BACKEND=dashscope
EMBEDDING_BACKEND=dashscope
LANGFUSE_PUBLIC_KEY=         # optional; falls back to MockObserver
LANGFUSE_SECRET_KEY=         # optional

Limitations

The demo knowledge base is in pipeline.py:KNOWLEDGE_DOCS; production should load documents from a managed store.
The MCP server is local and educational; production should add auth, audit logs, and tenant isolation.
The reviewer is a lightweight quality gate, not a formal safety system.

Enterprise Knowledge Assistant Skill

Architecture

User question -> Route guards + PlannerAgent -> one of: - Agentic RAG for HR/product/API knowledge - MCP tool call for orders, inventory, notifications - Direct answer or safe refusal -> ReviewerAgent quality check -> Final answer

All major steps are wrapped with Langfuse-compatible @observe spans. Full implementation notes are in reference/architecture.md.

When To Use

"入职 7 年有几天年假？"

"API 限流是多少？"

"StarLink 企业版多少钱？"

"查订单 ORD-002"

"SKU-A100 库存还有多少？"

The skill should refuse or defer when the knowledge base does not cover the topic, rather than guessing.

Programmatic Entry Point

from pipeline import upgraded_pipeline result = upgraded_pipeline("入职 7 年有几天年假？") print(result["path"]) print(result["answer"])

Return shape:

{ "path": "rag" | "mcp" | "direct", "answer": str, "review": str, }

Evaluation

Configuration

DASHSCOPE_API_KEY= # fill with your own key LLM_BACKEND=dashscope EMBEDDING_BACKEND=dashscope LANGFUSE_PUBLIC_KEY= # optional; falls back to MockObserver LANGFUSE_SECRET_KEY= # optional

Limitations

The demo knowledge base is in pipeline.py:KNOWLEDGE_DOCS; production should load documents from a managed store.

The MCP server is local and educational; production should add auth, audit logs, and tenant isolation.

The reviewer is a lightweight quality gate, not a formal safety system.

enterprise-knowledge-assistant