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+# Week 8 - LLM-First Agentic Deal Orchestrator
+
+## Goal
+
+The goal of this submission is to build a deal-evaluation workflow where **LLMs are the main decision engine**, not just an add-on.  
+Instead of using only fixed rules, the system uses LLM agents to:
+
+- estimate fair market value from deal context and comparable items
+- assign confidence and risk flags with structured outputs
+- decide the final next action (`alert_user`, `watchlist`, or `skip`)
+
+This makes the pipeline more realistic for agentic systems: evidence in, structured reasoning, explicit action out.
+
+## What this implements
+
+1. LLM valuation agent with strict JSON outputs (`estimated_value`, `confidence`, `rationale`, `risk_flags`)
+2. LLM decision agent with strict JSON outputs (`action`, `selected_deal_id`, `rationale`)
+3. `LLMFirstDealOrchestrator` class that runs the full agentic cycle
+4. Explainable deal cards generated from valuation outputs
+5. Feedback loop persisted to `feedback_log.json` and applied as prior context
+6. Deterministic fallback path when `OPENAI_API_KEY` is not available
+
+## Outcome
+
+The notebook demonstrates a complete LLM-first orchestration flow that is interpretable, feedback-aware, and safe to run locally.
+
+To run LLM orchestration cells, set `OPENAI_API_KEY` in your environment.
+
+Main file: `week8_exercise.ipynb`