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.
Let's explore how to implement this solution using the Multi-Agent Lightweight Framework for Amazon Bedrock.
Below is the high-level architecture of the multi-agent system:
To illustrate the capabilities of this system, letβs 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.
The following agents are orchestrated by a Supervisor Agent to ensure a structured and modular validation process:
Full details on the configuration can be found here : https://github.com/AmineIzanami/agentic-workflows-amazon-bedrock?tab=readme-ov-file#configuration
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
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 }
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"
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.
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.
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