AWS Transform Turns One – What the New Agents Mean for Modernization

One year ago we introduced AWS Transform as the first agentic AI service built to rewrite, refactor, and test enterprise code at scale. In the past twelve months the platform has processed 4.5 billion lines of code, saved 1.6 million developer hours, and helped customers migrate hundreds of thousands of servers. The anniversary brings three concrete updates that change how architects will design modernization pipelines.

Service update

• New agent families – Kiro, Claude, Cursor, and Codex are now available out‑of‑the‑box. Each family carries a different prompting style and model size, letting you pick the right balance of speed versus depth of analysis.
• Agent Builder Toolkit (Kiro Power) – A low‑code SDK that lets you author custom transformation agents. You can embed organization‑specific lint rules, proprietary framework upgrades, or security‑policy checks without leaving the Transform console.
• Full‑stack Windows & Mainframe Reimagine – Windows‑only transformation packs now include UI‑layer migration (WinForms → WPF/WinUI) and automated test generation for legacy COBOL modules.

Typical use cases

1. Framework upgrades – A financial services firm upgraded 12,000 .NET 6 services to .NET 8 with a single Transform job, cutting the effort from months to weeks.
2. Language migration – A media company moved 3,000 Python 2 scripts to Python 3.11, preserving custom data‑pipeline semantics through a custom Codex‑based agent.
3. Compliance refactoring – A health‑tech provider added GDPR‑compliant data‑masking logic to every API endpoint by attaching a Kiro‑Power rule that injects the masking call at compile time.

Trade‑offs to consider

• Model cost vs. precision – Larger agents (Claude, Codex) produce higher‑quality diffs but consume more compute credits. For bulk, low‑risk upgrades the Kiro agents are often sufficient.
• Custom agent maintenance – The SDK is powerful, yet each custom rule adds a dependency on the Transform runtime version. Teams must version‑control their agent definitions and test them against Transform upgrades.
• Security boundary – Transform agents run inside an AWS‑managed sandbox, but any custom code you ship to the sandbox is executed with the same permissions you grant. Follow the principle of least privilege when configuring the Transform execution role.

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Claude Platform on AWS – Native Access without a Separate Account

Anthropic’s Claude Platform is now available as a first‑class AWS service. The integration removes the need for a separate Anthropic account, consolidating billing and IAM controls under a single AWS organization.

Service update

• Direct console & API – Launch Claude models from the AWS Management Console, CloudFormation, or the AWS CLI.
• Early‑access beta features – Includes Claude‑3.5‑Haiku and a built‑in prompt‑tuning UI.
• Data residency – Customer data is processed outside the AWS security boundary, as per Anthropic’s policy. Encryption‑in‑transit and at‑rest is still enforced by AWS.

Use cases

• Customer‑support chatbots – Deploy a Claude‑based assistant behind Amazon Connect, leveraging the same VPC and IAM policies used for other contact‑center services.
• RAG pipelines – Combine Claude with Amazon Bedrock’s vector store to build knowledge‑base assistants for internal documentation.
• Creative coding – Use Claude to generate boilerplate code snippets that feed directly into an AWS CodePipeline.

Trade‑offs

• Data‑processing location – Because Anthropic processes prompts outside the AWS boundary, you must verify compliance with regulations that restrict data movement.
• Pricing model – Claude pricing on AWS mirrors the Anthropic marketplace rates; there is no discount for consolidated AWS spend.
• Feature parity – Some Anthropic‑only beta features may lag behind the native Claude console, so keep an eye on the release notes.

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Amazon EC2 M3 Ultra Mac – Apple Silicon at Scale

The M3 Ultra Mac instance family brings the Apple M3 Ultra silicon (28‑core CPU, 60‑core GPU, 32‑core Neural Engine, 256 GB unified memory) to the cloud. It is positioned as a direct upgrade to the M4 Max Mac instances.

Service update

• 2× unified memory, 1.75× CPU cores, 1.5× GPU cores, 2× Neural Engine compared with M4 Max.
• Available in both on‑demand and spot pricing; spot price is roughly 30 % lower than the M4 Max on‑demand rate.

Use cases

1. Xcode CI/CD – Run up to 12 parallel iOS simulators per instance, cutting build‑and‑test cycles from hours to minutes.
2. On‑device ML prototyping – Train CoreML models on the instance’s Neural Engine before shipping to devices.
3. High‑fidelity rendering – Use the 60‑core GPU for Metal‑based rendering jobs in a CI pipeline.

Trade‑offs

• Cost vs. concurrency – While the raw performance is higher, the per‑hour price is also higher than M4 Max. Teams should benchmark the number of simulators they actually need before scaling to many instances.
• License constraints – macOS instances require a macOS Developer Program license; the license cost is not covered by AWS.
• Regional availability – Currently limited to US‑East‑1, US‑West‑2, and EU‑Central‑1. Plan workloads accordingly.

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Redshift RG on Graviton – Faster, Cheaper Analytics

Amazon Redshift RG (Vectorized Query Engine) now runs on AWS Graviton CPUs. The new instance type delivers up to 2.4× performance on typical warehouse workloads and 30 % lower price per vCPU.

Service update

• Supports Apache Iceberg and Parquet natively on the compute nodes.
• Auto‑tuning of vectorized execution plans based on data‑skew patterns.

Use cases

• Hybrid data‑lake queries – Join data stored in S3 (Iceberg tables) with Redshift‑managed fact tables without data movement.
• BI dashboard refresh – Reduce nightly dashboard refresh times from 45 minutes to under 20 minutes.

Trade‑offs

• Migration effort – Existing RA3 clusters need a brief pause for a snapshot‑restore to an RG node type.
• CPU‑specific code – User‑defined functions compiled for x86 will need recompilation for Arm64.
• Instance size granularity – RG currently offers a narrower set of node sizes; large‑scale clusters may need to over‑provision.

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New Preview Capabilities

Feature	What it does	Typical scenario	Considerations
AWS Security Agent – Full Repository Scanning (preview)	Deep, context‑aware static analysis across the entire codebase; auto‑generates remediation patches.	Large monorepos with mixed languages where manual code review is a bottleneck.	Preview is free, but remediation patches should be reviewed before merge.
AWS Interconnect – OCI multicloud (preview)	Private, high‑throughput link to Oracle Cloud Infrastructure using the open Interconnect spec.	Data‑gravity workloads that need low‑latency access to OCI databases.	Still in preview; SLA is limited to 99.9 %.
Bedrock Advanced Prompt Optimization	Simultaneously compare original vs. optimized prompts across up to five models.	Teams migrating from older Claude prompts to newer Claude‑3.5 or testing cost‑efficient alternatives.	Optimization runs consume extra inference credits; monitor budget.

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Architectural Takeaways

1. Agentic AI is becoming a first‑class building block – Transform, Claude, and Bedrock prompt‑optimizers show that AWS is treating model‑driven code manipulation as a service you can wire into CI/CD pipelines.
2. Apple silicon in the cloud expands the macOS developer surface – The M3 Ultra Mac makes it feasible to run large‑scale iOS/macOS CI workloads without a physical device farm.
3. Arm‑based analytics are now mainstream – Redshift RG on Graviton demonstrates that vectorized query engines can extract significant performance gains from the efficiency of Arm cores.
4. Multicloud connectivity is moving from VPN‑style to private‑link style – Interconnect’s open spec means you can treat other clouds as extensions of your VPC, simplifying network topology.

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Looking Ahead

• Custom Transform agents will likely evolve into a marketplace where ISVs sell domain‑specific refactoring packs.
• Claude on AWS may integrate tighter with Bedrock, allowing unified prompt‑tuning across Anthropic and Amazon models.
• M3 Ultra Mac pricing hints at a future where Apple‑silicon instances become the default for any macOS‑centric workload.
• Redshift RG will probably be the foundation for a new generation of serverless analytics that automatically scales Arm compute based on query complexity.

Stay tuned for next week’s roundup where we’ll dive into the upcoming AWS Serverless Containers preview and the Quantum Computing Sandbox beta.