Real ChatGPT answer · index seedcwdj8uk95tkcompleteB2B software · AI and automation toolsinformational
What architectural patterns should I use when setting up multi step LangChain agents to ensure they do not hallucinate during tool execution
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What architectural patterns should I use when setting up multi step LangChain agents to ensure they do not hallucinate during tool execution
1 real search · 1 real-search pass
LangChain LangGraph official docs tool calling structured output middleware human in the loop state graphGoogle ↗
OBSERVEDOther / navigational
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.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
LangChain LangGraph official docs tool calling structured output middleware human in the loop state graph Google ↗
- Graph API overview · langchain-ai.github.io
- LangGraph overview · langchain-ai.github.io
- docs/src/oss/langchain/middleware/built-in.mdx at main · langchain-ai/docs · github.com
- docs/src/oss/langchain/human-in-the-loop.mdx at main · langchain-ai/docs · github.com
- middleware | langchain · reference.langchain.com
- LangGraph Graph API · docs.langchain.com
- LangChain Structured Outputs · docs.langchain.com
- Human-in-the-Loop · docs.langchain.com
- LangSmith Documentation · docs.smith.langchain.com