← Back to all announcements
★★★☆☆ 15/06/2026

AWS launches a new continuous modernization capability in AWS Transform that provides autonomous tech debt analysis and remediation at scale

Enterprises can now autonomously detect, prioritize, and fix tech debt across thousands of repos—including AI-readiness gaps—without manual effort.

View original announcement →

Visual Summary

graph TD A{{AWS Transform Continuous Modernization}}:::announced B((Enterprise Repos)):::external C([Tech Debt Analysis]):::feature D([Agentic Readiness]):::feature E([Scheduled Remediation]):::feature F(AWS Security Agent):::compute G(Kiro IDE):::compute H([Portfolio Visibility]):::feature I((Developers)):::external B ==>|"connects"| A A -->|"scans"| C A -->|"assesses"| D A -->|"triggers"| E A -->|"integrates"| F F -.->|"remediates vulnerabilities"| E I -->|"invokes via"| G G ==>|"embeds"| A A -->|"displays"| H E -.->|"human review"| I classDef announced fill:#ff9900,stroke:#ec7211,color:#fff,font-weight:bold classDef compute fill:#e3f2fd,stroke:#1565c0,color:#1565c0 classDef storage fill:#e8f5e9,stroke:#2e7d32,color:#2e7d32 classDef feature fill:#fff3e0,stroke:#e65100,color:#e65100 classDef external fill:#f5f5f5,stroke:#616161,color:#616161

What's New

AWS has launched a continuous modernization capability within AWS Transform (currently in Preview) that autonomously detects, prioritizes, and remediates technical debt across enterprise software portfolios at scale. The new capability extends Transform's existing modernization coverage—which already handles data center migrations, mainframe modernization, and version/runtime upgrades—to provide always-on codebase hygiene across thousands of repositories. It also introduces agentic readiness assessments and integrates with AWS Security Agent to address security vulnerabilities directly at the source code level.

How It Works

  • Source code ingestion: Customers connect repositories from GitHub, GitLab, Bitbucket, or other sources through the AWS Transform console, CLI, or IDE integrations, giving the service visibility across their entire codebase portfolio.
  • Autonomous analysis: The service continuously scans connected repositories to detect and prioritize tech debt, including outdated dependencies, deprecated APIs, runtime issues, and security vulnerabilities, without requiring manual triggering.
  • Agentic and modernization readiness: Beyond standard debt detection, the system can assess codebases for "agentic readiness" (suitability for AI agent consumption) and general modernization readiness, providing structured findings for each.
  • Security integration: Transform integrates with AWS Security Agent to identify and remediate security vulnerabilities at the source code level, closing the loop between security scanning and code remediation.
  • Scheduled remediation with human oversight: Customers can schedule automatic remediation workflows while retaining human review checkpoints, ensuring changes are validated before being applied.
  • Cross-surface context sharing: Job state and findings context are shared across the web console, CLI, IDE, and coding agents (via the AWS Transform skill), so engineers can review and act on results in their preferred environment.
  • Kiro integration: The AWS Transform Kiro power enables the capability to be invoked directly within the Kiro IDE, embedding modernization workflows into the developer's existing toolchain.

Why It's Important

  • Scale of tech debt management: Enterprises with thousands of repositories face an unmanageable manual burden keeping codebases current; autonomous detection and remediation fundamentally changes the economics of software maintenance.
  • Shift from reactive to continuous: Moving from periodic, manual debt reviews to always-on analysis means vulnerabilities and outdated dependencies are caught and fixed faster, reducing risk exposure windows.
  • AI-readiness as a first-class concern: The agentic readiness assessment directly addresses the emerging need to prepare codebases for consumption by AI coding agents and autonomous systems, a capability most enterprises lack today.
  • Security posture improvement: Integrating with AWS Security Agent to remediate vulnerabilities at the source—rather than just flagging them—closes a critical gap between security detection and developer action.
  • Developer productivity: By automating routine modernization tasks (version upgrades, runtime migrations, dependency updates), engineering teams can redirect effort toward feature development rather than maintenance.
  • Portfolio-wide visibility: Full visibility across thousands of repositories in a single console gives engineering leaders the prioritization data needed to make informed investment decisions about technical debt.

How It's Different

  • End-to-end autonomous remediation: Unlike traditional static analysis or dependency scanning tools that only report issues, Transform continuously detects and remediates tech debt, not just surfaces it.
  • Breadth of modernization scope: Competitors typically focus on a single dimension (e.g., dependency updates or security scanning); Transform covers version upgrades, runtime migrations, API migrations, language translations, Lambda runtime upgrades, and security remediation in one service.
  • Agentic readiness assessment: No widely available competing tool offers a structured assessment of whether a codebase is ready for AI agent interaction, making this a differentiated capability for enterprises adopting agentic architectures.
  • Native AWS ecosystem integration: Deep integration with AWS Security Agent, Kiro, and other AWS services creates a unified workflow that third-party tools cannot replicate without custom glue code.
  • Cross-surface context continuity: Shared job state across console, CLI, IDE, and coding agents eliminates context-switching friction that plagues multi-tool modernization workflows.
  • Human-in-the-loop scheduling: The combination of autonomous remediation with configurable human oversight checkpoints distinguishes it from fully automated tools that may apply changes without review, or fully manual tools that require constant human effort.

When to Prefer It

  • Large enterprise portfolios: Organizations managing hundreds to thousands of repositories where manual tech debt tracking is impractical and a centralized, automated solution is needed.
  • Regulated industries requiring audit trails: Teams that need human oversight and documented approval of code changes before they are applied, such as financial services or healthcare organizations.
  • Teams adopting agentic AI workflows: Enterprises preparing their codebases to be consumed or modified by AI agents, where agentic readiness assessment provides a structured path to compatibility.
  • Organizations with security-to-code gaps: Companies where security teams identify vulnerabilities but lack a streamlined path to get fixes into source code; the Security Agent integration bridges this gap.
  • Greenfield DevOps transformations: Teams modernizing their SDLC who want to embed continuous modernization as a standard pipeline stage rather than a periodic project.
  • Multi-language, multi-runtime environments: Enterprises with heterogeneous stacks (mixed languages, runtimes, and cloud-native patterns) that need a single tool to handle diverse modernization scenarios consistently.
  • AWS-centric shops already using Transform: Organizations already using AWS Transform for data center or mainframe migration who want to extend the same platform to ongoing codebase maintenance without adopting a separate toolchain.

Availability

  • Status: Currently available in Preview; not yet generally available (GA).
  • Supported regions: US East (N. Virginia) and Europe (Frankfurt) AWS Regions at launch.
  • Source control support: Compatible with GitHub, GitLab, Bitbucket, and other source code providers.
  • Access methods: Available via AWS Transform web console, CLI, AWS Transform Kiro power, and as a skill in third-party coding agents.
  • Pricing: Pricing details are available on the AWS Transform pricing page (aws.amazon.com/transform/pricing/); specific pricing model was not disclosed in the announcement.
  • Limitation: Regional availability is limited at Preview launch; additional regions are expected as the service progresses toward GA.

Tags

Servicesaws-transform
Typepreview-launchnew-feature
Conceptsagentic-aimlopscoding-assistant
Use Casesenterprisedeveloper-toolsdevopsmigration
GeographyAMERICASEMEA

Related Resources

AI Radar AWS

AWS AI/ML news — curated, researched, explained

An automated intelligence platform that curates, researches, and analyzes AWS AI/ML/GenAI announcements daily. Every report is backed by real research — the system reads linked blog posts and documentation to provide accurate, in-depth analysis.

How Each Report Is Generated

  1. Collection — Daily monitoring of the AWS "What's New" RSS feed
  2. Filtering — AI-powered relevance detection for AI/ML/GenAI topics
  3. Taxonomy Tagging — LLM-based classification across 6 dimensions
  4. Importance Scoring — Point-based system with tag bonuses (1-5 stars)
  5. Research Phase — Follows links to blog posts and documentation
  6. Report Generation — Claude Sonnet produces structured 6-section analysis
  7. Visual Summary — Claude Opus generates Mermaid diagrams for key items
  8. Publishing — Static website rebuilt and deployed via CloudFront

Features

  • Faceted filtering by service, type, concept, and more
  • Multi-dimensional taxonomy with 80+ tags across 6 dimensions
  • Geographic availability badges (Global, APJ, EMEA, AMER) with filtering
  • Timeline visualization of announcement volume
  • PDF export for offline reading
  • Mermaid visual summaries for key announcements
  • Daily automated updates — no manual curation
What makes this different: Each report involves a dedicated research phase where the system reads linked blog posts and AWS documentation pages. This produces analysis that goes beyond the original announcement text.

Technology

Built with Python, AWS Lambda, Amazon Bedrock (Claude Sonnet 4.6, Opus 4.6, Haiku 4.5), S3, CloudFront, WAF, EventBridge, and CDK.

Open Source

This project is open source. Fork it, customize it for your needs, and deploy your own instance.
📦 github.com/bbonik/ai-radar-aws

How Importance Scoring Works

Each announcement receives a point score based on multiple factors. The total score maps to a 1-5 star rating:

1★ < 2 pts 2★ ≥ 2 pts 3★ ≥ 3.5 pts 4★ ≥ 5 pts 5★ ≥ 6.5 pts

Point Breakdown

FactorPointsWhen
Core AI service (Bedrock, AgentCore, SageMaker AI)+4Service named in title
Key AI service (SageMaker, Kiro, QuickSight)+2Service named in title
Other AI-related service+1Default
Blog post link+3Link to aws.amazon.com/blogs/
GitHub samples link+2Link to github.com/aws*
Documentation link+1Link to docs.aws.amazon.com/
New model+1.5Tagged as "new-model"
New service+1Tagged as "new-service"
New feature+0.5Tagged as "new-feature"
Anthropic / OpenAI provider+2Provider explicitly mentioned
Instance / notebook announcement-2Hardware/capacity, not feature
Performance / pricing / security-0.5Incremental updates
Region expansion to APJ+1Expands to Asia Pacific
Region expansion (non-APJ only)-1.5Only expands to other regions

Geographic Relevance Badges

Each announcement card shows a small badge indicating whether the feature is available in your region:

🌐 Global Available in all regions
🌏 APJ Asia Pacific
🌍 EMEA Europe / Middle East / Africa
🌎 AMER Americas (US, Canada, South America)
No badge Geography unknown
How geography is detected: The system detects ALL geographies mentioned in each announcement. If the text mentions specific regions (Tokyo, Frankfurt, Oregon, etc.), the corresponding geography badges are shown. If it says "all regions" or is a new feature with no region specified, it gets the Global badge. Geography is also filterable — click a geo chip to see only announcements available in that region.