Tuesday, April 1, 2025

Can a conversational AI assistant powered by Mosaic AI’s framework be designed to autonomously interact with users and provide personalized support?

A single high-performing LLM is the key to decision-making. Based on the user's question, it understands which path it needs to take to route the decision flow. It can utilize multiple tools to perform certain actions, store interim results in memory, perform subsequent planning and finally return the result to the user.

Entity relationship diagram

Simple agent framework process flow

remark 

mosaic_agent.agent.web_search_tool ( ) RETURNS STRING LANGUAGE PYTHON DETERMINISTIC AS  $$ 

tools performing tasks

 instruments = (     UCFunctionToolkit(     )     .embrace(     )     .get_tools() )

d

 immediate = ChatPromptTemplate.from_messages(     [(         ),     ] )

 agent = create_tool_calling_agent(llm, instruments, immediate) 

  • return_product_price_details
  • web_search_tool

 End-to-end trace of the agent execution via MLflow Trace

ChatMessageHistoryRunnableWithMessageHistory

 agent = create_tool_calling_agent(llm, instruments, immediate) agent_with_chat_history = RunnableWithMessageHistory(     agent_executor, )

agent chat history

agent demo

 

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