We need to swap out a legacy legacy blue prism automation for a smarter python based llm workflow. Where do I even start with this architecture?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
We need to swap out a legacy legacy blue prism automation for a smarter python based llm workflow. Where do I even start with this architecture?
1 real search · 1 real-search pass
Temporal Python durable execution workflows activities retries official documentation LangGraph persistence human in the loop Prefect workflows documentationGoogle ↗
OBSERVED
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 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
Temporal Python durable execution workflows activities retries official documentation LangGraph persistence human in the loop Prefect workflows documentation Google ↗
- Temporal website · temporal.io
- LangGraph website · langchain.com
- Prefect website · prefect.io
- Pydantic documentation · docs.pydantic.dev
- Temporal Workflow · docs.temporal.io
- documentation/docs/develop/python/integrations/langgraph.mdx at main · temporalio/documentation · github.com
- Durable AI · docs.temporal.io