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Transformation Pathways
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Transformation Pathways
Agentic AI-driven application modernisation through intelligent reverse engineering workflows. Multi-agent architecture that autonomously analyses, transforms, and rebuilds cloud-native applications.
Enterprise modernisation follows a structured 3-phase approach. Each phase uses specialised AI agents that work collaboratively to extract institutional knowledge, fix technical debt, and rebuild applications while preserving critical business logic.
AI agents autonomously analyse the codebase, extract architecture patterns, dependencies, and business rules to build a complete picture of the legacy system.
73% of modernisation delays stem from incomplete understanding of legacy systems. Automated assessment reduces discovery time from weeks to minutes.
AWS Transform handles framework upgrades and API migrations per-tier. Fixes version upgrades (Java 8→21, Spring Boot 2→3), deprecated APIs, and security vulnerabilities.
Technical debt costs enterprises $1.52 trillion annually. Automated transformation reduces framework migration effort by 85-95%.
AI agents assess modernisation possibilities, reverse-engineer the codebase into formal specs, then IDE (Spec Driven Dev) autonomously rebuilds the application. Validation confirms business logic preservation.
Reverse engineering extracts institutional knowledge locked in decades-old code. Spec-driven AI rebuilds ensure nothing is lost. 60% of modernisation projects fail due to incomplete requirements — automated spec generation eliminates this risk.
Specialised AI agents — Analyzer, Generator, Evaluator, Refiner — work collaboratively across all 3 phases. Each agent has domain-specific tools via MCP protocol.
4 autonomous governance agents (Architecture, Compliance, Change, Risk) validate every transformation against enterprise standards, regulatory controls, and operational policies.
12-24 week manual cycles reduced to 2-4 hours. >90% efficiency gain. Business logic preservation validated through automated baseline-vs-validation test comparison.
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Configure your application, choose tiers, then customise the pathway steps.
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Build flows with Strands Agent, MCP Tool, LLM Prompt, and Condition nodes.
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Manage local agents, registered agents, and deployed runtimes
Select a pattern to guide your transformation. Think in buckets, not flat lists.
Live view of transformation pathways, agent activity, step execution, and outcomes.
Metrics for evaluating LLM-generated content quality, operational health, and the Generator→Validator→Human triplet feedback loop.
Manage local agents, registered agents, and deployed runtimes