How I built a comprehensive cloud engineering solution powered by Amazon Bedrock, MCP servers, and AWS Strands for automated operations, cost optimisation, root cause analysis, and intelligent infrastructure management
Introduction
Managing cloud infrastructure at scale requires constant monitoring, rapid response to issues, deep expertise across multiple AWS services, proactive cost optimisation, and comprehensive architectural guidance. What if you could have an AI-powered cloud engineer that never sleeps, automatically responds to errors, creates Jira tickets, generates pull requests, performs Well-Architected reviews, conducts root cause analysis, optimises costs, and provides expert guidance 24/7?
That's exactly what I built - a comprehensive Cloud Engineer Agent that combines the power of Amazon Bedrock's Claude model, Model Context Protocol (MCP) servers, and AWS Strands to create an intelligent, automated cloud operations platform accessible through Slack. This system goes far beyond a simple error response. It's a complete cloud engineering companion that handles everything from routine operations to complex architectural assessments.
Architecture Overview
Our solution represents a sophisticated multi-component architecture that seamlessly integrates various AWS services, external APIs, and AI capabilities:
Key Components Deep Dive
1. Multi-Input Architecture
Our system is designed to handle two primary input sources:
Slack Interface: Users can interact naturally with the Cloud Engineer Agent through Slack channels, asking questions about AWS services, requesting infrastructure changes, or seeking troubleshooting assistance.
CloudWatch Log Events: The system automatically monitors CloudWatch logs for errors and anomalies, triggering automated response workflows without human intervention.
2. AWS Strands Integration
At the heart of our Lambda function lies AWS Strands, which provides a powerful toolkit of integrated capabilities:
aws_cloudwatch: CloudWatch monitoring, logging, and alerting capabilities
aws_cost_explorer: Cost analysis, budget tracking, and optimisation recommendation
atlassian: Jira integration for issue tracking and project management
aws_eks: Elastic Kubernetes Service management and orchestration
aws_ecs: Elastic Container Service operations and container management
use_aws: Direct AWS service interactions and resource management
memory: Context retention and conversation history across sessions
3. MCP Server Architecture
AWS Documentation MCP Server: Maintains up-to-date access to AWS documentation, architectural patterns, and technical guides.
Atlassian MCP Server: Provides comprehensive Jira integration for automated ticket creation, project management, and workflow orchestration.
GitHub MCP Server: Enables seamless repository management, automated pull request creation, and version control integration.
AWS EKS MCP Server: Offers Kubernetes cluster management, pod orchestration, and container deployment capabilities.
Cost Explorer MCP Server: Delivers real-time cost analysis, billing insights, and resource optimisation recommendations.
CloudWatch MCP Server: Provides comprehensive monitoring, logging, and alerting services with custom dashboard creation and metric analysis.
4. Amazon Bedrock Integration
Amazon Bedrock's comprehensive suite powers AI capabilities:
Claude Model: Advanced language understanding and generation
Guardrails: Content filtering and safety validation
Knowledge Base: RAG implementation for internal knowledge repository
Enhanced Data Flow
The system follows a sophisticated data flow pattern:
Input Processing: Slack messages or CloudWatch log events trigger API Gateway
Lambda Orchestration: AWS Strands-powered Lambda processes requests using integrated tools
Service Integration: MCP Proxy provides load-balanced access to Fargate-hosted MCP servers
AI Processing: Amazon Bedrock processes requests with the Claude model and safety guardrails
Response Aggregation: Lambda combines responses from all integrated services
Output Delivery: Processed responses return to Slack, with automated Jira ticket creation and GitHub PR generation
CloudAgent Capabilities Showcase
Fast-Tracking Errors to Pull Requests
Time Reduction: 15-30 minutes → 2 minutes (87% faster)
Manual Process:
DETECT - Check alerts, user reports, monitoring (varies)
INVESTIGATE - Review logs, search errors, check metrics (varies)
LOCATE - Code review, recent changes, dependencies (varies)
ANALYSE - Root cause, trace flow, find pattern (varies)
IMPLEMENT - Write fix, code review, raise PR (varies)
DOCUMENT - Update JIRA, notify team, postmortem (varies)
CloudAgent Automation: Automated 9-step workflow from error detection to pull request creation:
Error simulation triggers the Lambda agent
MCP initialisation and root cause analysis
Automated issue creation and PR generation
Complete Slack integration for team notifications
Well-Architected Reviews at Speed
Time Reduction: 1-2 hours → 7 minutes (94% faster)
Manual Process:
Preparation - Identify instance and gather docs (15-30 mins)
Assessment - Review each pillar comprehensively (30-60 mins)
Documentation - Document findings, improvements & risks in Jira (15-30 mins)
CloudAgent Automation:
Comprehensive AWS Well-Architected Review completed automatically
Detailed analysis across all six pillars (Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimisation, Sustainability)
Automated findings documentation with specific recommendations
Priority-based action items with immediate implementation guidance
ClickOps Tracked NL Ops
Every action is traceable. Every command is accountable.
Demonstrated Capabilities:
Natural language AWS operations with full audit trails
Automated security group rule management with Jira ticket creation
Real-time execution tracking with detailed command logs
Complete integration with existing ITSM processes (Jira, Slack)
Example Operations:
"Open port 3389 on 'win_test' (Sydney) for 10.122.0.0/16. Create a Jira to track removal—temporary rule"
Automated security group modifications with rule IDs and descriptions
Immediate Jira ticket creation for tracking and compliance
Talk to the Cloud
Time Reduction: 30-60 minutes → ~5 minutes (92% faster)
Troubleshooting through conversation. No more digging through logs or CloudTrail
Manual Process:
Identify - Root cause from logs (10-20 mins)
Cross-check - CloudTrail events (10-20 mins)
Find related - metrics or alarms (5-10 mins)
Raising - Jira (5-10 mins)
CloudAgent Automation:
Conversational troubleshooting interface
Automatic log analysis and correlation
Real-time CloudTrail event investigation
Intelligent error pattern recognition
Automated ticket creation with complete context
Unified DevOps Intelligence via MCP Servers
Time Reduction: 30-60 minutes → ~5 minutes (92% faster)
Explore code, tickets, and changes - all through one interface
Manual Process:
Understand - the Framework (15-30 mins)
Search - GitHub PRs (15-30 mins)
Map - PRs to Framework (15-30 mins)
Verify - Jira Tickets (15-30 mins)
CloudAgent Automation:
Unified interface connecting GitHub, Jira, and compliance frameworks
Automated Enterprise Security & Compliance Framework analysis
Real-time PR mapping to security requirements
Comprehensive vulnerability assessments with remediation guidance
Colour-coded priority system (Critical/High/Medium/Low)
Predicting Spend, Made Simple
Real-time AWS cost forecasting through natural language queries
Demonstrated Capabilities:
"What is the forecasted cost for ECS clusters in the Sydney region?"
3-month cost predictions with confidence levels (80% confidence)
Monthly breakdown with range estimates
Historical usage pattern analysis
Automated trend identification and cost optimisation insights
Example Output:
Forecast Period: August 12 - November 12, 2025
Total 3-Month Forecast: $37.12 (range: $28.12 - $46.13)
Monthly breakdown with detailed range predictions
Trend analysis identifying cost patterns and optimisation opportunities
Rapid Compliance at Scale
Run a compliance check linked to the definition in Confluence, generate a PR + Jira ticket
Development Journey: Lessons Learned
The AI Tooling Revolution
This project showcased the power of modern AI development tools:
Product Development & Planning: Claude assisted with PRD creation and architectural planning
Large-Scale Development: Cline + Mantel API Gateway enabled rapid codebase development and refactoring
Documentation: Gemini generated comprehensive documentation from demo screenshots
Visual Assets: aws-diagram-mcp automated architecture diagram creation
Surgical Code Fixes: Amazon Q provided precise, targeted problem resolution
Development Acceleration: GitHub Copilot delivered real-time completions and commit message generation
System Prompt Engineering Challenges
Achieving surgical precision in automated responses required extensive system prompt refinement. The challenge was balancing comprehensive capabilities with focused execution - ensuring the agent could handle complex scenarios while maintaining minimal, targeted fixes for specific issues.
Multi-Agent vs. Single-Agent Architecture
Initial exploration of a multi-agent architecture revealed significant challenges:
Multi-Agent Challenges:
Context fragmentation across specialised agents
Over-specialisation leading to broader changes than necessary
Communication overhead and information loss during handoffs
Competing objectives between different agents
Single-Agent Superiority:
Complete context awareness without information fragmentation
Clear single objective focused on specific problem resolution
Simplified execution path, eliminating orchestration overhead
Consistent precision in delivering minimal, targeted changes
This architectural insight proved crucial for achieving surgical precision in automated error response workflows.
Future Roadmap
Planned enhancements include:
Enhanced RAG Implementation: Bedrock Knowledge Base or S3 Vector integration for improved contextual responses
Advanced Memory Management: Memory Strands tool for sophisticated context retention
Cost Intelligence: CloudWatch Dashboard integration for comprehensive cost monitoring
Enterprise Security: Advanced API security and authentication mechanisms
Conclusion
Building an AI-powered Cloud Engineer Agent represents a significant leap forward in cloud operations automation. By combining Amazon Bedrock's AI capabilities, MCP servers, and AWS Strands, I've created a system that not only responds to infrastructure issues but proactively manages and optimises cloud environments.
The key lessons learned - particularly around single-agent architecture superiority and the power of modern AI development tools - provide valuable insights for anyone building similar systems. The result is a comprehensive solution that transforms how teams interact with and manage their AWS infrastructure.
The future of cloud engineering lies in intelligent automation, and this architecture provides a robust foundation for organisations looking to scale their cloud operations while maintaining reliability, security, and cost effectiveness.