Here are the correct answers to your questions:

* To create an AI system that analyzes data, makes decisions, and stores memory
* To decide which execution path should be followed
* Traffic controller deciding routes
* To simplify user interaction and hide system complexity
* User Query → Intent → Routing → Analysis/Decision → Memory → Output
* It includes routing, tools, agents, and memory
* Intent detection and conditional routing
* Persistent business decisions stored in a database
* Pydantic structured output
* Task type (analysis / decision / summary)
* Sentiment analysis with urgency
* Type safety and predictable structure
* RunnableLambda
* System switches to summary or safe path
* Chain
* Understanding what the user wants
* Choose execution path based on conditions
* task == decision
* Mimic departments working simultaneously
* Customer and financial analysis
* Outputs merged and returned
* Chains
* Agents are slower and expensive
* Training
* Conditional chains
* Dictionary of outputs
* Turns a function into an LLM-callable tool
* Prevent unsafe SQL hallucinations
* Fetch enterprise data
* Acts like normal function
* Empty dictionary {}
* SQLite table
* decision_memory
* Persistent and auditable
* store_decision_memory
* SQL runs inside tools
* Only complex decisions
* Reason and decide
* Record final action
* RunnableLambda
* Tools require input
* Runtime error
* groq_llm.py
* Load environment variables
* SQLite decision memory
* Controlled interfaces to systems
* Complexity hidden behind chains
* Clear responsibility & maintainability
* Tool layer
* Decision-oriented AI system using LangChain