AgenticAi Efficiency Analysis Code

I need a well-structured code module written on top of the AgenticAi framework that takes our raw operational data streams and turns them into clear, actionable efficiency insights. The goal is straightforward: ingest the data we already collect on shop-floor events, equipment run-times, and throughput, then apply AgenticAi’s reasoning engine to surface bottlenecks, time-loss patterns, and improvement opportunities in real time.

You will work directly with the AgenticAi APIs (or its Python SDK) to:
• connect to our existing PostgreSQL warehouse and scheduled CSV drops,
• define the prompts, agent policies, and evaluation loops required for efficiency-focused analysis,
• return ranked recommendations with supporting metrics (e.g., ideal vs. actual cycle time, percentage idle, OEE deltas).

For acceptance I’ll run your code against a one-week historical slice and expect:
1. a reproducible Jupyter notebook or Python script,
2. concise README covering environment setup and configuration of AgenticAi keys,
3. a sample output report that highlights at least three efficiency gaps with calculated savings.

If you’ve previously paired AgenticAi (or LangChain, LlamaIndex, etc.) with manufacturing or service-operations datasets, mention it; otherwise, clarity of code and documentation will be the deciding factor.