AWS prescriptive-guidance documentation change
Summary
Updated Strands Agents SDK URL by removing '/latest/' path segment
Security assessment
The change is a simple URL format adjustment with no security context or implications mentioned. No evidence of addressing vulnerabilities or security features.
Diff
diff --git a/prescriptive-guidance/latest/strategy-operationalizing-agentic-ai/focus-areas-lifecycle.md b/prescriptive-guidance/latest/strategy-operationalizing-agentic-ai/focus-areas-lifecycle.md index d8aa95749..59d3d1108 100644 --- a//prescriptive-guidance/latest/strategy-operationalizing-agentic-ai/focus-areas-lifecycle.md +++ b//prescriptive-guidance/latest/strategy-operationalizing-agentic-ai/focus-areas-lifecycle.md @@ -19 +19 @@ Use feedback loops from observability data to initiate retraining, prompt tuning -Build a performance telemetry dashboard that shows decision accuracy, latency, cost, and reliability. To streamline and accelerate lifecycle management using AWS infrastructure, teams can use agent toolkits. One example is the [Strands Agents SDK](https://strandsagents.com/latest/), which provides structured tooling for prompt versioning, tool registration, and CI/CD integration with AWS services, such as [AWS CodePipeline](https://docs.aws.amazon.com/codepipeline/latest/userguide/welcome.html), [AWS Cloud Development Kit (AWS CDK)](https://docs.aws.amazon.com/cdk/v2/guide/home.html), and [AWS Lambda](https://docs.aws.amazon.com/lambda/latest/dg/welcome.html). Additionally, use [Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html) and [Amazon Elastic File System (Amazon EFS)](https://docs.aws.amazon.com/efs/latest/ug/whatisefs.html) for storing agent artifacts and training data. Use [AWS Step Functions](https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html) to automate complex retraining or validation workflows. You can use [Amazon SageMaker AI](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/what-is-sagemaker-unified-studio.html) when agents require custom model tuning or fine-tuning workflows beyond LLM orchestration. Lifecycle discipline transforms agents from experiments into durable, evolving assets. +Build a performance telemetry dashboard that shows decision accuracy, latency, cost, and reliability. To streamline and accelerate lifecycle management using AWS infrastructure, teams can use agent toolkits. One example is the [Strands Agents SDK](https://strandsagents.com/), which provides structured tooling for prompt versioning, tool registration, and CI/CD integration with AWS services, such as [AWS CodePipeline](https://docs.aws.amazon.com/codepipeline/latest/userguide/welcome.html), [AWS Cloud Development Kit (AWS CDK)](https://docs.aws.amazon.com/cdk/v2/guide/home.html), and [AWS Lambda](https://docs.aws.amazon.com/lambda/latest/dg/welcome.html). Additionally, use [Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html) and [Amazon Elastic File System (Amazon EFS)](https://docs.aws.amazon.com/efs/latest/ug/whatisefs.html) for storing agent artifacts and training data. Use [AWS Step Functions](https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html) to automate complex retraining or validation workflows. You can use [Amazon SageMaker AI](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/what-is-sagemaker-unified-studio.html) when agents require custom model tuning or fine-tuning workflows beyond LLM orchestration. Lifecycle discipline transforms agents from experiments into durable, evolving assets.