TL;DR

I built a Python-based autonomous agent that acts as a preliminary legal auditor. It scans service agreements, cross-references them with a set of corporate compliance policies (like payment terms and liability caps), and generates a risk assessment report. This isn’t just a script; it’s a look into how agentic workflows can tackle real-world business bottlenecks.

Source Code: https://github.com/aniket-work/autonomous-legal-auditor

Introduction

In my experience working with various project teams, I’ve observed that the "Legal Review" phase is often where agility goes to die. It’s not the lawyers’ fault; they are overwhelmed. I thought, "What if we could have a first-pass AI auditor that flags the obvious stuff?"

The goal wasn’t to replace the lawyer, but to empower the engineer or product manager to fix the glaring issues (like a missing governing law clause) before wasting legal’s time. This article documents my experiment in building such a tool.

What’s This Article About?

This is a technical walkthrough of building the Autonomous Legal Contract Auditor. It’s a command-line interface (CLI) tool that:

1. Ingests a contract (simulated as text).
2. Parses it against a "Rule Database" (e.g., Payment Terms < 45 days).
3. Outputs a detailed compliance report with pass/fail statuses.

I focused on creating a flexible architecture where rules can be added or modified easily, mocking the behavior of a sophisticated AI agent for this Proof of Concept (PoC).

Tech Stack

For this experiment, I kept the stack lightweight but robust:

• Python 3.12: The core logic.
• Rich: For building that beautiful, hyper-realistic terminal interface you saw in the GIF.
• PyYAML: For defining configuration and rules.
• Mermaid.js: For all the architectural diagrams.

Why Read It?

If you’re interested in how to structure a business-logic application, or if you want to see how to simulate complex "agentic" behavior with clean Python code, this is for you. In my opinion, understanding how to build these specific, domain-focused tools is becoming a critical skill for developers. Plus, you get to see some cool terminal UIs.

Let’s Design

Before writing a single line of code, I mapped out the system. I always find that a clear diagram saves hours of debugging later.

System Architecture

The user submits a document, which the Agent receives. The Agent then consults the "Compliance Engine"—a mix of rule definitions and logic evaluators—before returning a scored report.

The Audit Workflow

I visualized the interaction as a sequence where the Agent loops through rules. This loop is critical because it allows for granular reporting on specific failures rather than a generic "Bad Contract" error.

Let’s Get Cooking

Here is where I started assembling the pieces. I structured the project to separate the "Rule Engine" from the "Application Logic".

The Rule Definition

I decided to treat rules as data, not code. This way, we can add new compliance policies without rewriting the parser.

@dataclass
class ComplianceRule:
id: str
category: str
description: str
severity: str  # "CRITICAL", "HIGH", "MEDIUM", "LOW"
check_logic: str

RULES_DB = [
ComplianceRule(
id="PAY-001",
category="Payment Terms",
description="Payment terms must not exceed 45 days upon receipt of invoice.",
severity="HIGH",
check_logic="max_days_check"
),
# ... more rules
]

In my opinion, using dataclasses here creates a very clean, type-safe way to manage business logic.

The Analyzer Logic

This is the brain of the operation. In a full production version, this would call an LLM API. For this PoC, I simulated the analysis to demonstrate the architectural flow and the scoring mechanism.

def analyze_contract(self, contract_text: str) -> Dict[str, Any]:
results = {"findings": [], "score": 100}

for rule in self.rules:
# Simulation: In production, insert LLM or Regex logic here
has_violation = self._check_rule(rule, contract_text)

if has_violation:
results["findings"].append({
"rule_id": rule.id,
"status": "FAIL",
"severity": rule.severity,
"detail": f"Violation detected: {rule.description}"
})
results["score"] -= self._get_deduction(rule.severity)
else:
results["findings"].append({
"rule_id": rule.id,
"status": "PASS",
"severity": rule.severity,
"detail": "Compliant."
})

return results

The Interface

I used the rich library to present the data. As per my experience, a tool that looks professional is adopted 10x faster than one that dumps text to stdout.

def main():
console = Console()
# ... setup ...

# Analysis Animation
with Progress(...) as progress:
task1 = progress.add_task("Parsing natural language...", total=100)
# ... logic ...

# Display Results in a Table
table = Table(title="Compliance Findings", border_style="blue")
# ... add rows ...
console.print(table)

Let’s Setup

If you want to run this experiment yourself, here is the setup I used.

Clone the repository:

git clone https://github.com/aniket-work/autonomous-legal-auditor.git
cd autonomous-legal-auditor

Install dependencies:

pip install -r requirements.txt

Let’s Run

Executing the agent is simple. It loads the default mock contract and runs the audit.

python main.py

You should see the terminal come alive—parsing the text, checking the rules, and finally printing that satisfying dashboard of compliance stats.

Closing Thoughts

Building this Autonomous Legal Contract Auditor reinforced my belief that "Vertical AI Agent" tools—specialized for deep, specific tasks—are the future. Instead of a general chatbot, we built a focused tool that speaks the language of the domain (payment terms, liability, governing law).

I plan to extend this in the future by connecting it to a real local LLM to do actual text extraction. But for now, this PoC serves as a solid foundation for how business logic and AI flows can merge.

Disclaimer

The views and opinions expressed here are solely my own and do not represent the views, positions, or opinions of my employer or any organization I am affiliated with. The content is based on my personal experience and experimentation and may be incomplete or incorrect. Any errors or misinterpretations are unintentional, and I apologize in advance if any statements are misunderstood or misrepresented.