Scaling cloud migrations with agentic AI on Amazon Bedrock AgentCore
AWS Professional Services developed a multi-agent framework on Amazon Bedrock AgentCore that reduced IaC development time from 3–4 weeks per application to minutes across a portfolio of over 300 applications The framework uses the Strands Agents SDK and Model Context Protocol (MCP) to orchestrate four purpose-built agents across the migration lifecycle Three core bottlenecks addressed: manual discovery overhead, redundant infrastructure code development, and reactive post-migration operations Ag
Analysis
TL;DR
- AWS Professional Services developed a multi-agent framework on Amazon Bedrock AgentCore that reduced IaC development time from 3–4 weeks per application to minutes across a portfolio of over 300 applications
- The framework uses the Strands Agents SDK and Model Context Protocol (MCP) to orchestrate four purpose-built agents across the migration lifecycle
- Three core bottlenecks addressed: manual discovery overhead, redundant infrastructure code development, and reactive post-migration operations
- AgentCore Gateway converts existing APIs and Lambda functions into MCP-compatible tools, while AgentCore Identity provides scoped IAM authentication
- AgentCore Memory enables cross-agent context sharing, allowing the Intake Agent's discovery outputs to flow directly into the IaC Agent without manual handoff
Why It Matters
This represents a practical, production-grade implementation of agentic AI for enterprise cloud migration at scale, demonstrating measurable time savings that could transform how organizations approach data center exit programs. For AI practitioners, it showcases a real-world multi-agent orchestration pattern using MCP tool calling, session isolation, and shared memory—providing a reference architecture for building similar enterprise automation systems.
Technical Details
- Four specialized agents: Intake Agent (automated discovery and dependency mapping), IaC Agent (security-adherent infrastructure code generation), Migration Intelligence and Governance Agent (portfolio reporting, well-architected assessments across Jira/Confluence/Webex), and SRE Agent (proactive monitoring and automated remediation)
- Platform stack: Agents built with Strands Agents SDK, hosted on Amazon Bedrock AgentCore's serverless runtime with session isolation, powered by Amazon Bedrock foundation models
- Integration layer: AgentCore Gateway converts APIs, Lambda functions, and existing services into MCP-compatible tools; AgentCore Identity handles authentication via scoped IAM roles and identity providers
- Cross-agent communication: AgentCore Memory stores session state and shared context, enabling the Intake Agent to write target architecture and dependency mappings that the IaC Agent reads directly
- Complementary AWS services: AWS DMS for generative AI-assisted schema conversion and automated database cutover, AWS Transform for legacy application modernization
Industry Insight
- Multi-agent orchestration frameworks with shared memory and standardized tool protocols (MCP) are becoming a viable pattern for automating complex, multi-phase enterprise workflows beyond simple chatbot use cases
- The shift from manual, per-application engineering effort to agent-driven automation at scale (300+ applications) demonstrates that agentic AI can deliver order-of-magnitude productivity gains in infrastructure operations, not just content generation
- Enterprises should evaluate AgentCore-like platforms that combine session isolation, scoped IAM authentication, and cross-agent context sharing as foundational requirements for production-grade agentic systems handling sensitive migration workloads
Disclaimer: The above content is generated by AI and is for reference only.