Dokugent CLI introduces a **documentation-first** workflow for autonomous agents. Instead of just running scripts, it forces a state-management layer where every agent action is logged into a structured Markdown ledger, creating a "Paper Trail" for AI logic.

### The "Logic Ledger" System
At the core of Dokugent is the **Ledger Protocol**. Every time an agent (whether it's powered by Claude, GPT-4, or a local Mistral model) decides to execute a command or modify a file, Dokugent intercepts the intent and records:
- **The Prompt:** The exact instruction given to the agent.
- **The Reasoning (Chain of Thought):** The internal steps the agent took to reach a decision.
- **The Action:** The final command or code change.
- **The Verification:** A post-action check to ensure the intent was fulfilled.

### EQ Benchmarking & Simulations
Beyond technical tasks, Dokugent has been used to simulate complex human scenarios to measure **Emotional Intelligence (EQ)** in LLMs. By running scenarios like "grief support" or "conflict resolution" through the Dokugent wrapper, we can extract structured scores and empathy accuracy metrics, publishing them to a central leaderboard for model comparison.

### Agentic Sovereignty
Dokugent is built for the era of **Agentic Sovereignty**. It ensures that as we move toward multi-agent swarms, the human remain the high-level architect. By standardizing how different models report their work, Dokugent prevents vendor lock-in and maintains a persistent session history across multiple CLI executions.

### Key Objectives
- **Traceability:** Every action is linked to a specific human intent via a signed audit trail.
- **Context Persistence:** Maintains the 'mental model' of the project across different agent turns.
- **Human-in-the-Loop:** Provides a structured interface for human oversight without slowing down the agent's velocity.