Amazon Web Services has expanded its enterprise modernisation portfolio with the launch of new AI driven agents designed to help organisations update legacy applications, automate refactoring tasks and reduce long term technical debt. The new capabilities build on the company’s Transform service, which assists enterprises in migrating older systems to cloud native architectures. This latest update positions AI agents as core components of modernisation workflows, especially for businesses with extensive custom codebases accumulated over several decades.
Enterprises globally continue to face challenges when upgrading legacy systems. Many organisations still rely on monolithic applications, outdated programming languages and large volumes of custom written logic that are difficult to translate into modern software frameworks. AWS has stated that its AI agents are designed to automate a significant share of this work by analysing code, identifying modernisation opportunities and generating updated patterns that meet current architectural standards. The company notes that this approach can shorten migration timelines, reduce manual dependencies and improve consistency across development teams.
The AI agents operate by scanning application repositories, assessing code structure and mapping out functions that require transformation. They assist developers by recommending refactoring pathways, generating clean and modular code and identifying unused components that can be retired. According to AWS, these agents can handle a wide range of programming languages commonly used in legacy systems. They also integrate with developer environments, allowing engineers to validate generated code before deployment. This capability supports teams that need to modernise hundreds of applications simultaneously while maintaining reliability and accuracy.
Industry observers have pointed out that AI driven modernisation tools are becoming increasingly relevant as organisations accelerate digital transformation agendas. Large enterprises often face skill shortages when dealing with legacy languages and frameworks. Many of these systems require specialised knowledge that is becoming harder to find. AWS aims to address this problem by allowing teams to automate complex rewriting tasks and focus on higher value engineering work. The company states that its solution can significantly reduce the effort required for code conversion, dependency mapping and architectural restructuring.
Alongside refactoring support, the AI agents can perform static analysis, detect potential vulnerabilities and highlight areas where code efficiency can be improved. This contributes to more secure and stable application environments, especially for sectors that rely on long standing digital infrastructure such as financial services, telecom and government services. Applications in these sectors often run on outdated stacks but carry critical operational workloads. AWS notes that faster modernisation can help organisations minimise risks associated with ageing systems and lower maintenance costs.
The Transform service now includes features that automate documentation generation, enabling enterprises to maintain clear records of code behaviour, system logic and architectural changes. Documentation gaps have historically slowed down modernisation projects, particularly when legacy systems lack comprehensive records. By generating documentation along with modern code, the AI agents support easier onboarding for developers and smoother transitions to cloud environments.
Reports indicate that early adopters have used the service to reduce manual effort in large migration projects. Some organisations have begun testing the agents for converting legacy services into microservice based architectures. Others have applied them to break down monolithic applications into modular components, which are easier to test, deploy and scale. AWS has positioned these tools as suitable for long term modernisation initiatives where hundreds of applications must be updated over time.
Businesses that depend heavily on custom written code stand to benefit the most from these AI capabilities. Custom applications often evolve over years and contain complex dependencies that are difficult to unwind. Automated analysis can highlight interlinked modules, redundant patterns and areas where modularisation would improve performance. For enterprises planning multi year transformation programs, these insights could reduce rework and allow teams to prioritise workloads more effectively.
AWS has also discussed how the AI agents integrate with existing cloud native development practices. Teams using continuous integration and continuous delivery pipelines can incorporate automated code improvements directly into their development lifecycle. This ensures that updated code follows cloud friendly patterns and maintains alignment with industry standards. Over time, this can help organisations adopt more scalable architectures and reduce the operational overhead associated with maintaining legacy environments.
Industry analysts believe that the introduction of AI agents for code modernisation signals a broader trend in enterprise technology. As companies integrate generative AI into internal operations, modernisation workflows are likely to shift from manual code rewriting to assisted automation. Cloud providers are expected to continue expanding AI driven development features that reduce technical debt and optimise large codebases. This reflects a growing need among enterprises to modernise faster as competition, regulatory requirements and digital customer expectations increase.
The new capabilities in AWS Transform are expected to support organisations preparing for large scale cloud migration projects in 2025 and beyond. With the continued rise of enterprise AI adoption, automated modernisation tools may become standard components of cloud transformation strategies. AWS believes that its AI agents can play a central role in enabling customers to evolve legacy environments into more efficient, agile and secure systems while controlling costs and reducing risk.
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