Transform Enterprise Architecture Reviews with AI: From Months to Hours Using CrewAI and Amazon Bedrock AgentCore

The article describes an innovative approach to transforming enterprise architecture reviews from a months-long manual process into an efficient automated system using Agentic Workflow using CrewAI and Amazon Bedrock AgentCore. The solution employs specialized AI agents (Technology Analyst and Enterprise Architect) to conduct comprehensive architectural assessments while maintaining security standards and delivering significant business outcomes.

Balu Mathew

Published Sep 24, 2025

Last modified Sep 25, 2025

Application architecture reviews serve as critical gatekeepers of system integrity, security, and scalability within enterprises. Typically, large enterprises undergo multiple architecture review cycles before any application goes live – initial phase, pre-deployment, and pre-production validation. These processes often rely on extensive spreadsheet-based workflows that require constant manual updates. Multiple stakeholders across different departments such as Enterprise Security, Enterprise Architecture, Compliance, and Product Council must provide input, leading to inconsistent documentation formats and standards. The time-consuming data collection from disparate systems, coupled with version control challenges among multiple reviewers, creates significant overhead in the review process.

This blog explores how Generative AI can transform a weeks-long manual architecture review process into an efficient, automated system that maintains rigorous security standards while delivering measurable business outcomes.

Solution Overview: Agentic Architecture Reviews

Automating complex tasks such as enterprise application reviews demands more than just powerful LLMs and data connections – it requires an orchestrated network of specialized AI agents. Our solution applies multiple AI personas: a Technology Analyst and an Enterprise Architect, working in tandem to deliver comprehensive architecture assessments spanning data analysis, security validation, and technology risk evaluation.

Enterprise architecture reviews in many organizations are guided by standardized questionnaires and templates maintained by Architecture Review Boards. These comprehensive documents capture organizational standards across security controls, infrastructure patterns, operational excellence, and compliance requirements. Development and product teams must complete these templates as part of the mandatory architecture review process, ensuring all solutions align with enterprise standards before deployment. Here's a glimpse into how these standards are structured in a typical Technology Architecture Review Documentation template:

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### Architecture Review Documentation 



#### Security Controls & Compliance

- Authentication Design

  - Identity Provider Integration: [AWS Cognito/Azure AD/Okta]

  - MFA Implementation: [Required for all admin access]

- Data Protection

  - Classification: [DC2/DC3]

  - Encryption Standards: [AES-256 for data at rest]



#### Infrastructure Design

- High Availability Pattern

  - Multi-AZ Deployment: [Yes/No]

  - Target RPO/RTO: [RPO < 15min, RTO < 1hr]

  - Failover Strategy: [Active-Active/Active-Passive]

Traditionally, completing these templates/sheets/webforms has been a time-consuming process, requiring days from multiple SMEs and creating bottlenecks in agile delivery pipelines. Our AI-powered review system transforms this process by automatically analyzing documentation, code, and infrastructure configurations to populate and validate the templates against enterprise standards. This shifts teams' focus from documentation to critical architectural decisions while maintaining governance compliance.

CrewAI is a framework for orchestrating autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks through coordinated multi-agent workflows. In the context of enterprise architecture reviews, CrewAI enables us to create specialized AI agents such as Technology Analyst, Enterprise Architect and Security Architect that mirror the expertise of human reviewers while operating at machine speed and scale.

Key Components

Technology Analyst Agent: Specializes in AWS Well-Architected principles, identifying security risks, performance bottlenecks, and architectural anti-patterns
Enterprise Architect Agent: Ensures compliance with organizational standards, security frameworks (SOC 2, PCI DSS, GDPR), and governance policies
Integration Tools: Connects with enterprise systems like GitHub, Jira, and CMDB for comprehensive data collection

Solution Implementation

Prerequisites

Before running the Solution, ensure you have:

• Python 3.12+ installed • Required packages are installed • Access to Amazon Bedrock models

Installation

# Install dependencies

pip install uv crewai crewai-tools

Environment Configuration

Create a .env file with your configuration:

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# AWS Configuration
AWS_REGION=your_aws_region
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key

# LLM Configuration
MODEL=bedrock/http://us.anthropic.claude-sonnet-4-20250514-v1:0
MAX_TOKENS=4000
TEMPERATURE=0.0

# Enterprise Data Sources
GITHUB_ORG=your-github-organization
GITHUB_TOKEN=github-token

# Application Configuration
APP_ID=AP001330 # Your application identifier

Define Crew and Tools

Define your agents - Each agent has a specific role to play

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# agents.yaml
technology_analyst:
  role: Senior Technology Analyst
  goal: Analyze {system_name} architecture using AWS Well-Architected principles and identify critical security risks
  backstory: You are a seasoned technology analyst with and you identify the factual and relevant information and create a detailed report.
enterprise_architect:
  role: Enterprise Architect
  goal: Ensure {system_name} meets enterprise standards, security requirements, and approved patterns
  backstory: You are an enterprise architect with deep knowledge of organizational standards, compliance frameworks (SOC2, PCI-DSS, GDPR), and security requirements

Define corresponding tasks for each agent

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# tasks.yaml
technology_analysis_task:
  description: Identiy all corresponding artifacts belonging {system_name} and Craft a architecture report with System Purpose, Architectre diagrams and components using AWS Well-Architected principles. Focus on security, operational and reliability risks.
  expected_output: Technical analysis with security risks, AWS Well-Architected assessment, and architectural recommendations.
  agent: technology_analyst
enterprise_architecture_review_task:
  description: Create comprehensive enterprise architecture review for {system_name} based on technical analysis. Include Summary, Findings, Criticality, and Recommendations.
  expected_output: Detailed report with Applicsation Details, Archityecture, Components, Summary, Findings, Criticality and Recommendations.
  agent: enterprise_architect

Running the Review

Execute architecture reviews locally:

# Run the crew locally

crewai run

Sample Output

The system generates a comprehensive enterprise architecture review document. Here's an excerpt from the review process.

You can see the agents working together, gathering required data from their tools like Github and generating a comprehensive report.

Scaling for the Enterprise: Remote Deployment of Agents via Amazon Bedrock AgentCore Runtime

Now we need to run the solution in a distributed, scalable and secure way for enterprise usage. Amazon Bedrock AgentCore Runtime provides a secure and isolated runtime to deploy and run our agents at scale, eliminating the need to handle complex orchestration, packaging, security, observability while offering native AWS integration and enterprise-grade security features.

Solution Architecture

Amazon Bedrock AgentCore Features used

· Agent Runtime: Foundational component of AgentCore – containerized application which hosts or process the Ai agent or tool code.

· Endpoints: Access points for your runtime to invoke your agents

· Authentication and Security: Amazon Bedrock AgentCore Identity provided both inbound and outbound Authn and securit6y and supports multiple protocols such as OAuth

· Integrations via Gateway: Amazon Bedrock AgentCore Gateway provides an easy and secure way to build, deploy and connect to tools at scale.

· Starter toolkit for seamless Deployment operations: The starter toolkit is a Command Line Interface (CLI) toolkit that you can use to deploy AI agents to an AgentCore Runtime.

Deployment Process

The deployment process involves configuring and packaging the agents for Amazon Bedrock:

Prerequisites:

Configure the required IAM permissions as per AgentCore IAM permissions document

# Install Dependencies

pip install bedrock-agentcore bedrock-agentcore-starter-toolkit

Create source file for Agent Invocation

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# archreview_agent.py



from bedrock_agentcore.runtime import BedrockAgentCoreApp



app = BedrockAgentCoreApp()

@app.entrypoint

def agent_invocation(payload, context):

    """Handler for agent invocation"""

    print(f'Payload: {payload}')

    try: 

        # Extract user message from payload with default

        user_message = payload.get("prompt", "Enterprise Architecture Review")

        print(f"Processing topic: {user_message}")

        # Create crew instance and run synchronously

        review_crew_instance = ArchReview()

        crew = review_crew_instance.crew()

        # Use synchronous kickoff instead of async - this avoids all event loop issues

        result = crew.kickoff(inputs={'topic': user_message})

        print("Context:\n-------\n", context)

        print("Result Raw:\n*******\n", result.raw)

        # Safely access json_dict if it exists

        if hasattr(result, 'json_dict'):

            print("Result JSON:\n*******\n", result.json_dict)

        return {"result": result.raw}

    except Exception as e:

        print(f'Exception occurred: {e}')

        return {"error": f"An error occurred: {str(e)}"}



if __name__ == "__main__":

    app.run()

Deploy to Amazon Bedrock AgentCore Runtime

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# Configure deployment using Starter Toolkit
agentcore configure -e archreview_agent.py

# Launch the agent
agentcore launch

Invoke the Agent

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# Invoke the agent
agentcore invoke '{"prompt": "AP001330"}'

Business Outcomes and ROI

Accelerated Time-to-Market: Review timeline: Months → Hours (95% reduction), Self-service capability at SDLC phase
Increased Developer Productivity : Reduction in manual documentation effort, Automated data collection from multiple enterprise systems, Standardized review templates and outputs
Enhanced Security Posture: Higher accuracy in identifying critical security vulnerabilities • Consistent application of enterprise security policies
Standardization Benefits : Consistent evaluation standards across all reviews, centralized policy enforcement, version-controlled documentation and audit trails

Conclusion and Next Steps

Think about how much time architects and developers spend reviewing system designs - it's often weeks or months of back-and-forth emails, meetings, and spreadsheets. The solution aims to make their lives easier by using AI to handle the heavy lifting of architecture reviews.

At its core, the system uses two AI agents that work together: one acts as a Technology Analyst looking at technical details, while the other serves as an Enterprise Architect ensuring everything aligns with company standards. These agents, powered by Amazon's Bedrock and CrewAI, can quickly scan through documentation, check security requirements, and identify potential issues that humans might miss. Amazon Bedrock AgentCore provides the enterprise scale, security and capabilities to build, deploy and run similar Agentic Applications.

The key here isn't just about speed or automation; it's about making the whole architecture review process less painful and more consistent for everyone involved, while maintaining the high standards needed for enterprise systems.

# agentic-ai # amazon-bedrock-agentcore # amazon-bedrock # crewai # enterprise

Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.

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Transform Enterprise Architecture Reviews with AI: From Months to Hours Using CrewAI and Amazon Bedrock AgentCore

Balu Mathew