⚡ Introduction

Enterprises are overwhelmed with document-heavy workflows: invoices, contracts, reports, RFPs. These documents are often unstructured or semi-structured, requiring intelligent parsing, classification, and decision-making.

With the advent of Large Language Models (LLMs), we now have the opportunity to build smart, automated workflows that go beyond basic OCR and NLP pipelines. But a single LLM prompt isn't enough for enterprise-grade reliability.

That's where multi-agent systems shine — they allow us to decompose complex tasks into specialized roles, enabling modularity, resilience, and scalability.

In this article, we'll walk through how to design and implement a production-grade multi-agent LLM system for legal contract review , using LangChain , FastAPI , and modern DevOps practices.

Use Case Overview: Legal Contract Review

We'll simulate a contract ingestion pipeline that:

1. Classifies the type of contract (NDA, Employment, Vendor, etc.)

2. Extracts key clauses (e.g., termination, payment terms)

3. Checks compliance (e.g., presence of required clauses)

4. Generates a summary for the legal team

Each step is handled by a dedicated agent, communicating through a controller — forming a modular, testable, and scalable architecture .

🧠Step 1: Setting Up Agents

ClassifierAgent

from langchain_openai import ChatOpenAI

class ClassifierAgent:
    def __init__(self, llm: ChatOpenAI):
        self.llm = llm

    def classify(self, doc_text: str) -> str:
        prompt = f"""
        What type of legal document is this?
        Options: NDA, Employment Contract, Vendor Agreement, Other
        Document:
        {doc_text[:1000]}
        """
        response = self.llm.invoke(prompt).content.strip()
        return response

ClauseExtractorAgent

class ClauseExtractorAgent:
    def __init__(self, llm: ChatOpenAI):
        self.llm = llm

    def extract(self, doc_text: str) -> dict:
        prompt = f"""
        Extract the following clauses: Termination, Payment Terms, Jurisdiction.
        Return as JSON.
        Document:
        {doc_text[:1500]}
        """
        response = self.llm.invoke(prompt).content.strip()
        return self._parse_json(response)

    def _parse_json(self, raw: str) -> dict:
        # Add robust error handling here
        try:
            return json.loads(raw)
        except json.JSONDecodeError:
            raise ValueError("Failed to parse clause extraction output.")

Step 2: Creating the Agent Controller

class DocumentProcessingController:
    def __init__(self, agents: dict):
        self.classifier = agents['classifier']
        self.extractor = agents['extractor']

    def run(self, doc_text: str) -> dict:
        try:
            doc_type = self.classifier.classify(doc_text)
            clauses = self.extractor.extract(doc_text)
            return {
                "doc_type": doc_type,
                "clauses": clauses
            }
        except Exception as e:
            # Log and rethrow for centralized error handling
            logger.error(f"Pipeline failed: {str(e)}")
            raise

Step 3: FastAPI Interface

from fastapi import FastAPI, UploadFile, HTTPException
from pydantic import BaseModel
import logging

app = FastAPI()
logger = logging.getLogger(__name__)

class DocumentResponse(BaseModel):
    doc_type: str
    clauses: dict

@app.post("/process", response_model=DocumentResponse)
async def process_document(file: UploadFile):
    if not file.filename.endswith(('.pdf', '.docx', '.txt')):
        raise HTTPException(status_code=400, detail="Unsupported file format")

    try:
        text = await file.read()
        result = controller.run(text.decode('utf-8'))
        return result
    except Exception as e:
        logger.exception("Document processing failed")
        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")

Step 4: Dockerfile for Deployment

FROM python:3.10-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .

EXPOSE 8080
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]

🌐Scaling: Adding Compliance & Summary Agents

ComplianceAgent

class ComplianceAgent:
    def check(self, clauses: dict) -> dict:
        prompt = f"""
        Check if the following clauses meet company policy:
        {json.dumps(clauses)}
        Return missing clauses or warnings.
        """
        response = self.llm.invoke(prompt).content.strip()
        return self._parse_compliance_result(response)

    def _parse_compliance_result(self, raw: str) -> dict:
        # Implement structured parsing logic
        ...

SummarizerAgent

class SummarizerAgent:
    def summarize(self, doc_text: str) -> str:
        prompt = f"""
        Provide a concise summary of the legal document below (max 200 words):
        {doc_text[:3000]}
        """
        return self.llm.invoke(prompt).content.strip()

🧩Persistence and State Management

Use MongoDB or PostgreSQL to store:

• Processed documents

• Agent outputs

• Audit logs

• User feedback

Example schema for storing results:

{
  "document_id": "uuid",
  "filename": "nda_contract_v1.pdf",
  "timestamp": "2025-04-05T14:30:00Z",
  "classification": "NDA",
  "clauses": {
    "termination": "...",
    "jurisdiction": "..."
  },
  "compliance_status": "partial",
  "summary": "..."
}

🧪 Testing Strategy

Unit Test Example (pytest)

def test_classifier_agent():
    mock_llm = MockLLM(response="NDA")
    agent = ClassifierAgent(mock_llm)
    result = agent.classify("This agreement is between two parties...")
    assert result == "NDA"

Integration Test

def test_full_pipeline():
    response = client.post("/process", files={"file": ("test.txt", b"This is an NDA...")})
    assert response.status_code == 200
    assert response.json()["doc_type"] == "NDA"

🧰 CI/CD Automation with GitHub Actions

Add a .github/workflows/deploy.yml file:

name: Deploy Contract Processor

on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.10'
      - name: Install dependencies
        run: pip install -r requirements.txt
      - name: Run tests
        run: pytest
      - name: Build Docker image
        run: docker build -t contract-processor .
      - name: Push to container registry
        run: |
          docker tag contract-processor your-registry/contract-processor:latest
          docker push your-registry/contract-processor:latest

📈 Observability and Monitoring

• Structured Logging : Use structlog or loguru for searchable logs.

• Metrics : Expose Prometheus metrics for latency, success rates, and error counts.

• Tracing : Integrate with Jaeger or OpenTelemetry for tracing agent interactions.

• Alerting : Use Grafana or Datadog to monitor performance and failures.

🤝 Collaboration and Knowledge Sharing

A production system should be built to scale across teams:

• Modular Codebase : Each agent lives in its own module (agents/classifier.py, etc.)

• Shared SDKs : Create reusable libraries for common LLM patterns.

• Documentation : Write clear docstrings and use Swagger UI for API docs.

• Prompt Versioning : Store prompts in a version-controlled config system.

Building a multi-agent LLM system for document processing in the enterprise is no small task. It requires a blend of AI expertise, software engineering rigor, and operational know-how.

But by structuring your code modularly, writing tests, automating deployments, and adding observability, you're showing the world that you can build real-world, production-grade AI applications .

This kind of project will not only help automate business processes but also stand out on your GitHub profile when applying for ML, AI, or backend engineering roles.

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