What is the best way to set up LangChain agents to handle customer support tickets without hallucinating incorrect refund policies
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What is the best way to set up LangChain agents to handle customer support tickets without hallucinating incorrect refund policies
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
LangChain documentation retrieval augmented generation agents structured output tools human in the loop guardrails current Google ↗
LangGraph documentation workflows vs agents persistence human-in-the-loop structured output Google ↗
- docs/src/oss/langgraph/overview.mdx at main · langchain-ai/docs · github.com
- docs/src/oss/langgraph/workflows-agents.mdx at main · langchain-ai/docs · github.com
- docs/src/oss/langgraph/persistence.mdx at main · langchain-ai/docs · github.com
- docs/src/oss/langchain/guardrails.mdx at main · langchain-ai/docs · github.com
- docs/src/oss/langchain/human-in-the-loop.mdx at main · langchain-ai/docs · github.com
- langgraph · langchain-ai.github.io
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
LangChain documentation retrieval augmented generation agents structured output tools human in the loop guardrails current OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
LangGraph documentation workflows vs agents persistence human-in-the-loop structured output OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |