Category: Artificial Intelligence

Introduction Migration assessment and planning is rarely a single-pass exercise. Inventory data is incomplete; assumptions shift as more stakeholders get involved, and new scenarios emerge as the strategy evolves. Traditional assessment workflows struggle with this reality because they treat each assessment as a one-shot report: collect data, run analysis, deliver a static business case, and […]

The term ‘mainframe modernization’ is itself out of date. Modernization used to mean upgrading the technology. AI has made it about reimagining the business. Introduction The term “mainframe modernization” has been associated with infrastructure and technology modernization projects rather than business transformation. This covers a wide range of projects with very different objectives: application and […]

An AI migration means different things to different teams, and that ambiguity is expensive. Are you migrating with AI, to AI, or from AI? The answer shapes your staffing plan, governance model, and timeline, because each motion carries a different risk profile and demands a different set of skills. Gartner found that by the end […]

Today, we’re announcing the model-to-model migration assessment custom transformation for AWS Transform, an AI agent that assesses your generative AI applications and produces a comprehensive migration plan for moving from other GenAI providers to Amazon Bedrock. Enterprises that built generative AI on OpenAI, Google Gemini, or direct Anthropic APIs find that scaling to production demands more than […]

Traditional VMware migrations have long suffered from fragmentation and inflexibility. Enterprises had to cobble together disconnected tools for discovery, planning, network configuration, and rehosting, each requiring specialized expertise and lengthy manually orchestrated handoffs. These rigid, rules-based tools can’t adapt to each customer’s unique environment, resulting in compounding costs, expensive customizations, and timelines that stretch into […]

Introduction AWS Transform Custom (ATX) automates code modernization at scale. What makes it different from running an AI coding assistant on each repo is that it learns. Each execution captures patterns, fixes, and edge cases as reusable knowledge, so transformations get faster and more reliable with every run. If you’ve got hundreds of Java 8 […]

A year ago, we launched AWS Transform with one mission: modernize the world’s infrastructure and applications. 12 months in, here’s what 4.5+ billion lines of code processed, hundreds of thousands of servers migrated, and saving customers 1.6+ million hours taught us about migration, modernization, and why this work cannot wait. Why this matters more than […]

Migrating hundreds of servers from an on-premises VMware environment to AWS involves coordinating across teams, accounts, and tools. You need to set up your target environments, plan waves, deploy replication agents, monitor their progress, run tests, and perform cutover, often across multiple AWS accounts. Each step can be challenging on its own, requiring careful planning […]

Introduction If you’re migrating code at scale across hundreds of repositories, you need reproducible results across runs. Large language model (LLM)-based agents, by definition, rely on the probabilistic nature of LLMs where each result is generated from a variety of potentially probable outcomes. In a short-horizon task, this nondeterminism is manageable. In a long-horizon task […]

AWS Transform for mainframe replatform is a migration service that helps organizations accelerate the modernization of their mainframe workloads to AWS. As organizations pursue their mainframe modernization journey, migrating mainframe systems to AWS has become a strategic imperative. The service provides an end-to-end automated migration pathway, including tooling, guidance, and self-managed runtime environments with Rocket […]