Abstract

Statements of Work are crucial documents that outline the scope, deliverables, timelines, and costs of a project. However, creating and validating SoWs can be time-consuming and error-prone, involving multiple stakeholders and requiring expertise across various domains.

Key Features

• Customizable multi-agent orchestration using Supervisor and Collaborator Agents.
• Lambda-backed AI tools for retrieving and analyzing documents or any other capabilities required through custom ad-hoc code.
• Seamless integration with Amazon Bedrock’s foundation models.

Methodology

Key Benefits of Multi-Agent Systems for SoW Processing:

• Consistency: AI agents apply the same rigorous standards to every document, reducing human error.
• Specialized Expertise: Different agents focus on specific aspects of the SoW, ensuring comprehensive coverage.
• Collaboration: Agents work together, sharing insights and flagging potential issues.
• Scalability: The system can easily adapt to handle more complex SoWs or additional validation requirements.
• Consistency: AI agents apply the same rigorous standards to every document, reducing human error.

Implementing an Agentic AI Solution for SoW Processing

Let's explore how to implement this solution using the Multi-Agent Lightweight Framework for Amazon Bedrock.

Architecture 🏗️

Below is the high-level architecture of the multi-agent system:

• Supervisor Agent: Coordinates workflow execution and assigns tasks.
• Collaborator Agents: Handle specialized tasks like text validation, compliance checks, and image analysis.
• Knowledge Base & RAG: Retrieves relevant content to enrich responses.
• Amazon Bedrock Agents: Execute AI-driven tasks using Claude, Titan, and other foundation models.
• Agent Tools (Lambda Functions): Provide auxiliary services such as document retrieval and image analysis.

Example Use Case 📑

To illustrate the capabilities of this system, let’s explore an automated contract validation use case. In this scenario, multiple agents work together to process, validate, and analyze a Statement of Work (SoW) document.

Workflow

The following agents are orchestrated by a Supervisor Agent to ensure a structured and modular validation process:

1. Analyse Image Document Agent 📷
   Extracts and analyzes images from the SoW document to provide visual insights.
   Uses the Claude 3 Sonnet model for enhanced image interpretation.
2. Structural Compliance Agent 📄
   Ensures the document follows standard structural and formatting guidelines.
   Verifies adherence to predefined SoW templates that have been shared through a prompt
3. Technical Scope Validation Agent 🏗️
   Validates the technical feasibility of the SoW.
   Ensures all technical specifications are aligned with best practices.
4. Business Financial Validation Agent 💰
   Evaluates the financial aspects of the contract.
   Reviews cost breakdowns, investments, and budget allocations.
5. Risk & Compliance Agent ⚖️
   Identifies potential risk factors and compliance issues.
   Flags areas that may require additional due diligence.
6. Delivery Milestones Validation Agent📆
   Ensures that project milestones and delivery schedules are well-defined.
   Verifies if deliverables are achievable within the proposed timelines.
7. AI Consistency Agent 🤖
   Validates AI-related elements within the SoW.
   Ensures correct AI model selection and feasibility.
8. AWS Architecture Validation Agent 🏛️
   Analyzes AWS architecture diagrams for best practices compliance.
   Extracts AWS service details and validates security considerations.
9. Supervisor Agent 🎯
   Orchestrates the workflow execution and aggregates responses from all agents.
   Ensures smooth collaboration between specialized agents.

Detailed AWS Architecture:

Full details on the configuration can be found here : https://github.com/AmineIzanami/agentic-workflows-amazon-bedrock?tab=readme-ov-file#configuration

Step 1: Define Your Agents

Create a YAML configuration file to define your specialized agents. Here's an example:

agents:
  - name: StructuralComplianceAgent
    description: "Ensures the document follows standard structural and formatting guidelines."
    model: anthropic.claude-v2
  - name: TechnicalScopeValidationAgent
    description: "Validates the technical feasibility of the SoW."
    model: anthropic.claude-v2
  - name: BusinessFinancialValidationAgent
    description: "Evaluates the financial aspects of the contract."
    model: anthropic.claude-v2
  - name: RiskComplianceAgent
    description: "Identifies potential risk factors and compliance issues."
    model: anthropic.claude-v2
  - name: SupervisorAgent
    description: "Orchestrates the workflow and aggregates responses."
    model: anthropic.claude-v2

Step 2: Implement Custom Tools

Develop Lambda functions to handle specific tasks, such as document retrieval or data analysis. Here's a simple example:

import boto3

def lambda_handler(event, context):
    s3 = boto3.client('s3')
    bucket = event['bucket']
    key = event['key']
    
    response = s3.get_object(Bucket=bucket, Key=key)
    document_content = response['Body'].read().decode('utf-8')
    
    return {
        'statusCode': 200,
        'body': document_content
    }

Step 3: Configure the Workflow

Define the workflow in your YAML configuration:

workflow:
  - step: "Retrieve SoW Document"
    tool: "RetrieveDocument"
    agent: "SupervisorAgent"
  - step: "Analyze Structure"
    agent: "StructuralComplianceAgent"
  - step: "Validate Technical Scope"
    agent: "TechnicalScopeValidationAgent"
  - step: "Evaluate Financials"
    agent: "BusinessFinancialValidationAgent"
  - step: "Assess Risks"
    agent: "RiskComplianceAgent"
  - step: "Compile Final Report"
    agent: "SupervisorAgent"

Results

The Impact on SoW Processing

By implementing this agentic AI solution, businesses can expect:

• Faster Processing: AI agents can work simultaneously, dramatically reducing review time.

• Improved Accuracy: Specialized agents catch details that humans might miss.

• Consistency: Every SoW is subjected to the same rigorous review process.

• Scalability: The system can handle increased volume without compromising quality.

Looking Ahead

As AI technology evolves, we can anticipate even more sophisticated SoW processing capabilities. Future enhancements might include:

• Natural Language Generation: AI agents could draft sections of the SoW based on project requirements.

• Real-time Collaboration: Integration with communication platforms for immediate stakeholder feedback.

• Predictive Analytics: AI could forecast potential project risks based on SoW content.