Create README.md (Model Card)

README.md

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----
-license: mit
----
+---
+license: mit
+tags:
+- n8n
+- llm-agents
+- automation
+- gemini-2.5-flash
+---
+
+# Minimal LLM Council (AI Audit System)
+
+## Overview
+This project implements a "Council of Agents" architecture to audit LLM responses. It uses 3 parallel agents to generate answers and 2 rubric-based judges to score them for **Safety** and **Clarity**.
+
+**Files:**
+* `LLM_Council_Audit_Workflow.json`: The n8n workflow file.
+* `audit_log_proof.png`: Evidence of the persistent Google Sheets logging.
+
+## Key Features
+* **Multi-Agent Generation:** Orchestrates 3 Gemini agents (Cautious, Optimist, Logic).
+* **Parallel Judgment:** Judges evaluate answers *without* generating new content.
+* **Structured Output:** Returns a JSON object with `Confidence`, `Risks`, and `Citations`.
+* **Persistent Logging:** Asynchronously logs all decisions to Google Sheets.
+
+## Proof of Audit Log
+The system maintains a permanent record of every judgment:
+![Audit Log](audit_log_proof.png)
+
+## Design Decision (Intentionally Not Automated)
+**Decision:** I intentionally did **not** automate the final "blocking" action. The system detects risks and returns them to the client, but it does not silently refuse to answer.
+
+**Why:** Automated safety gating is a "black box." By returning the raw *Risk Assessment* to the client, we align with the philosophy of "tying scale to proof." We prove the risk exists via the Audit Log, but we allow the client-side policy (or human reviewer) to decide the final display threshold. This ensures transparency and prevents over-censorship.
+
+## Note on Model Selection
+For this implementation, I utilized **Google Gemini 2.5 Flash** for all nodes to ensure high-speed inference and access to frontier model capabilities. The "independence" of the agents is achieved through strict **System Prompt Engineering** (Persona-based differentiation: Cautious, Optimist, and Logic) rather than architectural heterogeneity.