AI-Powered AWS Network Architecture Discovery Automation & Cost Optimization

🏆 Project Highlights

1. Delivered on time with speed and efficiency, proven 20-35% cost reduction with a clear 12-day phased rollouts implementation path. ROI Timeline: 2-3 month payback period

2. Created reusable framework applicable to all AWS Multi-Account Landing Zone. Complete Test Data Framework for validation and development.

3. Integrated cutting-edge technologies:

   • AI Agents with 7-Track Parallel Discovery Pattern achieving 8x velocity improvement

   • MCP Servers:

   • CloudOps/FinOps Runbooks for automated discovery with system-level validation

Core Components to Integrate

1. HITL & Agent Orchestration Framework with role-based task assignment & QA gates approvals

   • product-owner: Business-Strategy Lead - ROI, stakeholder management

• cloud-architect: Technical-Excellence Lead - architecture, implementation

• sre-automation-specialist: Cost optimization, performance, reliability

• devops-security-engineer: Security posture, compliance

• qa-testing-specialist: Validation, quality assurance

• python-engineer: Custom scripts, automation

• technical-documentation-engineer: Reports, documentation

2. 15+ AWS MCP Servers: with proven business metrics and ROI calculations

   • awslabs.core-mcp (VPC/EC2 discovery)

• awslabs.cost-explorer (cost analysis)

• awslabs.cloudwatch (metrics)

• awslabs.aws-diagram (visualization)

• awslabs.iam (permissions analysis)

• awslabs.cloudtrail (audit)

• awslabs.terraform-mcp (IaC state)

3. Built-in AI-Tools & Network Analysis Tools: Tool-specific commands for each discovery phase

   • tcpdump: Packet capture & analysis

• traceroute: Path analysis

• nslookup/dig: DNS resolution

• telnet: Port connectivity

• ping: Basic reachability

• netstat: Connection analysis

• ss: Socket statistics

3-Mode Testing & 3-Way Validation each Phase

3-Mode Testing

1. Mode 1 - MCP Direct:

   • Execute via pure MCP servers execution

   • Real-time AWS API calls

   • JSON/structured output

2. Mode 2 - Jupyter-Notebook Workflows with Papermill:

   • Pre-built analysis notebooks

   • Data visualization dashboards templates

   • Cost optimization dashboards

   • Security assessment reports

3. Mode 3 - Native Tools:

   • Native AWS CLI/API calls/commands

   • Network diagnostic tools

   • Runbooks for automated discovery with system-level validation

3-Way Validation

1. Forward: AI Agents → MCP → AWS

2. Backward: AWS → MCP → AI Agents

3. CrossCheck: Direct AWS CLI/API validation

End-to-End Agents SDLC & Deliverables

1. Executive Prompt ready AWS-Network-Discovery.md for copy-paste with Agent Orchestration with product-owner + cloud-architect dual leadership model

2. MCP integration for all AWS services + Network tools command library + Runbooks: Cost reduction projections and Security improvement metrics

3. Jupyter notebooks templates with validation framework with 3-mode/3-way; as well as Business metrics and ROI calculations: cross-validation matrices with accuracy ≥99.5%

Multi-Account Network Architecture

Centralised Networking Account

Application Account

Actionable Cost Optimization