---
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_screenshot.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.