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@@ -5,7 +5,7 @@ <atom:link href="https://aws.amazon.com/blogs/machine-learning/feed/" rel="self" type="application/rss+xml"/> <link>https://aws.amazon.com/blogs/machine-learning/</link> <description>Official Machine Learning Blog of Amazon Web Services</description>-
<lastBuildDate>Tue, 01 Sep 2026 19:13:35 +0000</lastBuildDate>+
<lastBuildDate>Wed, 02 Sep 2026 21:32:10 +0000</lastBuildDate> <language>en-US</language> <sy:updatePeriod> hourly </sy:updatePeriod>@
@@ -13,119 +13,256 @@ 1 </sy:updateFrequency> <item>-
<title>Introducing Claude Fable 5.1 on AWS</title>-
<link>https://aws.amazon.com/blogs/machine-learning/introducing-claude-fable-5-1-on-aws/</link>+
<title>Accessing OpenAI models on Amazon Bedrock from Australia with global cross-Region inference</title>+
<link>https://aws.amazon.com/blogs/machine-learning/accessing-openai-gpt-5-6-models-on-amazon-bedrock-from-australia-with-global-cross-region-inference/</link> -
<dc:creator><![CDATA[Dani Mitchell]]></dc:creator>-
<pubDate>Tue, 01 Sep 2026 19:12:43 +0000</pubDate>+
<dc:creator><![CDATA[Frank Huang]]></dc:creator>+
<pubDate>Wed, 02 Sep 2026 21:22:05 +0000</pubDate> <category><![CDATA[Amazon Bedrock]]></category> <category><![CDATA[Announcements]]></category> <category><![CDATA[Intermediate (200)]]></category>-
<guid isPermaLink="false">fe5a761d13fdea1cd6343d646c1e042d79f66815</guid>+
<guid isPermaLink="false">262427adeddcaf49102f989ea5bfb8332e685f6c</guid>-
<description>Claude Fable 5.1 is now available on Amazon Bedrock and Claude Platform on AWS. This post covers Claude Fable 5.1's improvements, the Enterprise Frontier Safeguards for keeping your data in a cloud environment you control, and how to start building with the model on Amazon Bedrock.…-
<content:encoded><p>Today, we’re excited to announce the availability of Claude Fable 5.1 on Amazon Bedrock and Claude Platform on AWS. Claude Fable 5.1 delivers frontier intelligence for ambitious tasks across coding, scientific research, and enterprise workflows.</p> -
<p>Given its capabilities, Anthropic has designated Fable 5.1 a <em>Covered Model</em>, a category of Claude models that carry additional data retention, safety review, and access policies wherever they’re offered. For more information, see Amazon Bedrock <a href="https://docs.a…-
<p>This post covers Claude Fable 5.1’s improvements, the Enterprise Frontier Safeguards, and how to start building with the model on Amazon Bedrock.</p> -
<h2 id="what-makes-claude-fable-5.1-different">What makes Claude Fable 5.1 different</h2> -
<p>Anthropic reports Fable 5.1 is a clear improvement over Fable 5 on the hardest reasoning tests Anthropic runs. These tests include competition mathematics, graduate-level questions in engineering and the sciences, and long multi-step problems where reliability is key. In day-to-day use that…+
<description>Australian teams can now access OpenAI GPT-5.6 Sol, Terra, and Luna models on Amazon Bedrock with global cross-Region inference from the Asia Pacific (Sydney) and Asia Pacific (Melbourne) Regions. This post shows how to invoke the models, use prompt caching, set up Codex with OpenI…+
<content:encoded><p>Australian teams working with OpenAI models can now access the latest OpenAI models through Amazon Bedrock. Amazon Bedrock offers OpenAI <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/global-cross-region-inference.html" target="_blank" rel="noope…+
<p>In this post, we show you how to use prompt caching to optimize your inference cost, set up Codex with OpenID Connect (OIDC)-based authentication, and monitor usage with Amazon CloudWatch and Coding Agent Insights.</p> +
<h2 id="gpt-5.6-global-inference-profiles">GPT-5.6 global inference profiles</h2> +
<p>The following table lists the three global profile IDs and the Australian source Regions covered in this post.</p> +
<table border="1px" width="100%" cellpadding="10px"> +
<tbody> +
<tr> +
<td><strong>Global inference profiles</strong></td> +
<td><strong>Source Regions</strong></td> +
<td><strong>Destination Regions</strong></td> +
</tr> +
<tr> +
<td>global.openai.gpt-5.6-sol global.openai.gpt-5.6-terra global.openai.gpt-5.6-luna</td> +
<td>Asia Pacific (Sydney) ap-southeast-2 Asia Pacific (Melbourne) ap-southeast-4</td> +
<td>Routes to supported commercial AWS Regions</td> +
</tr> +
</tbody> +
</table> +
<p>Profile membership and model availability can change. Check <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference-support.html" target="_blank" rel="noopener">Cross-Region inference support</a> before deployment.</p> +
<h2 id="getting-started">Getting started</h2> +
<p>Before proceeding with this section, you can follow the <a href="https://aws.amazon.com/blogs/machine-learning/introducing-cross-region-inference-for-openai-gpt-5-6-models-on-amazon-bedrock/" target="_blank" rel="noopener">GPT-5.6</a> blog post to set up the following prerequisi…<ul> -
<li><strong>Agentic coding.</strong> Carries more of a project on its own, from code base-spanning features to code review and performance work, across multi-hour sessions. It is also more honest: if it gets stuck it says so, and it is less likely to disable a failing test to pass…-
<li><strong>Autonomous operation.</strong> Built for multi-hour jobs that span many applications. It plans, uses the tools it needs, recovers when a step fails, and keeps you updated without being asked.</li> -
<li><strong>End-to-end knowledge work.</strong> Takes an analysis from first question to finished document, doing the research, building the spreadsheet, writing the memo or deck, and checking its numbers as it goes. Built for everyday finance, accounting, and healthcare work.<…-
<li><strong>Scientific research.</strong> Supports research campaigns from literature and hypotheses to models, experiments, and formal verification.</li> -
<li><strong>Improved usability.</strong> Keeps you updated on long tasks, writes more clearly, and follows instructions more closely.</li> +
<li>An AWS account with Asia Pacific (Sydney) or Asia Pacific (Melbourne) enabled as your source Region.</li> +
<li>If your organization uses service control policy (SCP), verify that the SCP allows the GPT-5.6 global inference profiles in the selected source Region.</li> +
<li>An AWS Identity and Access Management (IAM) role or user with proper permissions to invoke the GPT-5.6 inference profiles.</li> +
<li>Python 3.9 or later with the <code>openai</code>, <code>boto3</code>, and <code>aws-bedrock-token-generator</code> packages installed.</li> </ul> -
<h2 id="data-retention">Data retention</h2> -
<p>Because Claude Fable 5.1 is a <a href="https://support.claude.com/en/articles/15425695-covered-models" target="_blank" rel="noopener">Covered Model</a>, its use is subject to data retention for up to 30 days and human review by Amazon personnel. With Amazon Bedrock, you can cont…-
<h2 id="enterprise-frontier-safeguards">Enterprise Frontier Safeguards</h2> -
<p><a href="https://www.anthropic.com/news/enterprise-frontier-safeguards" target="_blank" rel="noopener">Enterprise Frontier Safeguards</a> (EFS), built in partnership between AWS and Anthropic, will help eligible customers use Claude Fable 5 and Claude Fable 5.1 models while keep…-
<p>If you are an EFS-eligible customer, you can use Claude Fable 5 and Claude Fable 5.1 with zero data retention (ZDR) on Amazon Bedrock and Claude Platform on AWS. This is available for internal use through December 31, 2026. Additional Enterprise Frontier Safeguards will be available later t…-
<h2 id="getting-started-with-claude-fable-5.1-on-amazon-bedrock">Getting started with Claude Fable 5.1 on Amazon Bedrock</h2> -
<p>To try Fable 5.1, open the <a href="https://console.aws.amazon.com/bedrock/" target="_blank" rel="noopener">Amazon Bedrock console</a>, go to <strong>Test &gt; Playground</strong>, and select Fable 5.1 as the model. From there, you can run a prompt directly again…-
<p>Programmatically, you can call the model with the <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html" target="_blank" rel="noopener">Anthropic Messages API</a> against <code>bedrock-runtime</code> (through th…-
<h3 id="prerequisites">Prerequisites</h3> -
<ol type="1"> -
<li>Active AWS account with Amazon Bedrock access.</li> -
<li>AWS Command Line Interface (AWS CLI) installed and configured.</li> -
<li>Python 3.10+.</li> -
<li>Boto3 installed: <code>pip install boto3</code>.</li> -
<li>AWS Identity and Access Management (IAM) permissions: <code>bedrock:InvokeModel</code>and <code>bedrock:InvokeModelWithResponseStream</code>.</li> -
</ol> -
<p>Here’s a quick example using the AWS SDK for Python (Boto3):</p> +
<p>In the following section, we show you how to verify the active global inference profiles with both the AWS Command Line Interface (AWS CLI) and the Amazon Bedrock console.</p> +
<p><strong>Using the AWS CLI.</strong> The following commands list the active GPT-5.6 profiles and inspect the Terra profile from the Sydney Region. You can also use the commands with Sol or Luna by changing the inference profile ID. To run the same check from Melbourne Region, rep…+
<div class="hide-language"> +
<pre><code class="language-bash">aws bedrock list-inference-profiles \+
--region ap-southeast-2 \+
--type-equals SYSTEM_DEFINED \+
--query "inferenceProfileSummaries[?contains(inferenceProfileId, 'openai.gpt-5.6')].[inferenceProfileId,status]" \+
--output table+
+
aws bedrock get-inference-profile \+
--region ap-southeast-2 \+
--inference-profile-identifier global.openai.gpt-5.6-terra</code></pre> +
</div> +
<p><strong>Using the Amazon Bedrock console.</strong> Open the Amazon Bedrock console, select Sydney or Melbourne as your Region and choose Inference profiles under Infer, and filter for Global OpenAI GPT-5.6 Terra. The following screenshot shows the active profile from the Sydney …+
<div style="width: 810px" class="wp-caption alignnone">+
<a href="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/08/31/ML-21760-1.png" target="_blank" rel="noopener"><img src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/08/31/ML-21760-1.png" alt="Amazon Bedrock inferenc…+
<p class="wp-caption-text">Figure 1: The active GPT-5.6 Terra global inference profile in Sydney</p>+
</div> +
<h3 id="invoke-gpt-5.6-through-amazon-bedrock-runtime">Invoke GPT-5.6 through Amazon Bedrock Runtime</h3> +
<p>GPT-5.6 supports three access paths on the Amazon Bedrock Runtime endpoint: the OpenAI Responses API, OpenAI Chat Completions API, and Amazon Bedrock Converse API. The OpenAI-compatible APIs are called on the <code>/openai/v1</code> paths of this endpoint rather than through the…+
<p><strong>OpenAI Responses API.</strong> For applications that already use the OpenAI SDK with the Responses API, you can point the client at the Regional Amazon Bedrock Runtime endpoint.</p> +
<div class="hide-language"> +
<pre><code class="language-python">from aws_bedrock_token_generator import provide_token+
from openai import OpenAI+
+
region = "ap-southeast-2" # Use "ap-southeast-4" for Melbourne.+
model_id = "global.openai.gpt-5.6-terra"+
prompt = (+
"In three short bullet points, explain how Availability Zones "+
"help make an AWS application highly available."+
)+
+
openai_client = OpenAI(+
base_url=f"https://bedrock-runtime.{region}.amazonaws.com/openai/v1",+
api_key=provide_token(region=region),+
)+
+
responses_result = openai_client.responses.create(+
model=model_id,+
input=prompt,+
max_output_tokens=300,+
)+
+
print(responses_result.output_text)</code></pre> +
</div> +
<p>For streaming output, set <code>stream=True</code> and iterate over the response events. The following example prints text as it arrives:</p> +
<div class="hide-language"> +
<pre><code class="language-python">response_stream = openai_client.responses.create(+
model=model_id,+
input=prompt,+
max_output_tokens=300,+
stream=True,+
)+
+
for event in response_stream:+
if event.type == "response.output_text.delta":+
print(event.delta, end="", flush=True)</code></pre> +
</div> +
<p><strong>OpenAI Chat Completions API.</strong> You can also work with the Chat Completions API if your application already uses that.</p> +
<div class="hide-language"> +
<pre><code class="language-python">from aws_bedrock_token_generator import provide_token+
from openai import OpenAI+
+
region = "ap-southeast-2" # Use "ap-southeast-4" for Melbourne.+
model_id = "global.openai.gpt-5.6-terra"+
prompt = (+
"In three short bullet points, explain how Availability Zones "+
"help make an AWS application highly available."+
)+
+
openai_client = OpenAI(+
base_url=f"https://bedrock-runtime.{region}.amazonaws.com/openai/v1",+
api_key=provide_token(region=region),+
)+
+
chat_result = openai_client.chat.completions.create(+
model=model_id,+
messages=[{"role": "user", "content": prompt}],+
max_completion_tokens=300,+
reasoning_effort="low",+
)+
+
print(chat_result.choices[0].message.content)</code></pre> +
</div> +
<p><strong>Amazon Bedrock Converse API.</strong> Use Converse API when your application calls Amazon Bedrock through an AWS SDK. Boto3 resolves credentials through the standard AWS credential chain.</p> <div class="hide-language"> <pre><code class="language-python">import boto3-
import json-
# Create a Bedrock Runtime client-
bedrock_runtime = boto3.client(-
service_name="bedrock-runtime",-
region_name="us-east-1"+
region = "ap-southeast-2" # Use "ap-southeast-4" for Melbourne.+
model_id = "global.openai.gpt-5.6-terra"+
prompt = (+
"In three short bullet points, explain how Availability Zones "+
"help make an AWS application highly available.")+
messages = [+
{+
"role": "user",+
"content": [{"text": prompt}],+
}+
]-
# Invoke Claude Fable 5.1-
response = bedrock_runtime.invoke_model(-
modelId="global.anthropic.claude-fable-5-1",-
contentType="application/json",-
accept="application/json",-
body=json.dumps({-
"anthropic_version": "bedrock-2023-05-31",-
"max_tokens": 4096,-
"messages": [-
{-
"role": "user",-
"content": "An S3 bucket serves 40 TB/month egress. Estimate the monthly egress cost at $0.09/GB, and state one architecture change to cut it. Show the calculation, keep it under 120 words."-
}-
]-
})+
bedrock_client = boto3.client("bedrock-runtime", region_name=region)+
converse_result = bedrock_client.converse(+
modelId=model_id,+
messages=messages,+
inferenceConfig={"maxTokens": 300},)-
result = json.loads(response["body"].read())-
# Fable 5.1 is a reasoning model: the response may include a thinking block-
# before the text block, so select the text block rather than a fixed index.-
print(next(b["text"] for b in result["content"] if b["type"] == "text"))</code></pre> +
print(converse_result["output"]["message"]["content"][0]["text"])</code></pre> </div> -
<p>You can explore the <a href="https://github.com/aws-samples/anthropic-on-aws/blob/main/notebooks/claude_fable_5_1_getting_started/claude-fable-5-1-getting-started.ipynb" target="_blank" rel="noopener">Getting Started notebook</a> for more examples.</p> -
<h2 id="availability">Availability</h2> -
<p>Claude Fable 5.1 is available today on Amazon Bedrock through the US Geo CRIS (us.) and Global CRIS (global.) inference profiles. In AWS GovCloud (US), it is available on both the bedrock-runtime and bedrock-mantle endpoints. See the <a href="https://docs.aws.amazon.com/bedrock/latest/us…-
<p>Give Claude Fable 5.1 a try in the <a href="https://console.aws.amazon.com/bedrock" target="_blank" rel="noopener">Amazon Bedrock console</a>, in <a href="https://console.aws.amazon.com/claude-platform/" target="_blank" rel="noopener">Claude Platform on AWS</a>, or e…+
<p>For streaming output, use <code>converse_stream</code> with the same Region and profile ID, then iterate over the returned event stream.</p> +
<div class="hide-language"> +
<pre><code class="language-python">stream_result = bedrock_client.converse_stream(+
modelId=model_id,+
messages=messages,+
inferenceConfig={"maxTokens": 300},+
)+
+
for event in stream_result["stream"]:+
if "contentBlockDelta" in event:+
delta = event["contentBlockDelta"]["delta"]+
if "text" in delta:+
print(delta["text"], end="", flush=True)+
print()</code></pre> +
</div> +
<p>The preceding examples call Amazon Bedrock in Asia Pacific (Sydney). To run the same example for Asia Pacific (Melbourne), set the Region to <code>ap-southeast-4</code> and run the code.</p> +
<h3 id="working-with-prompt-caching">Working with prompt caching</h3> +
<p>GPT-5.6 prompt caching is available through the supported APIs. GPT-5.6 supports two caching modes on Amazon Bedrock. Implicit caching is enabled by default, and no code changes are required, while with explicit caching, you can define the reusable prefix, cache boundary, and cache key. <…+
<h2 id="setting-up-codex-with-gpt-5.6-on-amazon-bedrock">Setting up Codex with GPT-5.6 on Amazon Bedrock</h2> +
<p>Codex can use the same global inference profiles through Amazon Bedrock Runtime. Install the latest Codex CLI to use the native Amazon Bedrock Runtime model provider. Here, we validated the following configuration with codex-cli 0.149.1 using GPT-5.6 Sol from Asia Pacific (Sydney).</p>…+
<div class="hide-language"> +
<pre><code class="language-bash">npm install -g @openai/codex@alpha+
codex --version</code></pre> +
</div> +
<p>For organizations whose identity provider is Okta, Auth0, Microsoft Entra ID, Amazon Cognito, or AWS IAM Identity Center, the <a href="https://github.com/aws-samples/sample-openai-on-aws/tree/main/aws-oidc-auth" target="_blank" rel="noopener">AWS OIDC Auth Helper repository</a> …+
<div class="hide-language"> +
<pre><code class="language-ini">[profile &lt;AWS_OIDC_PROFILE&gt;]+
credential_process = &lt;ABSOLUTE_PATH_TO_CREDENTIAL_PROCESS&gt; --profile &lt;OIDC_HELPER_PROFILE&gt;+
region = ap-southeast-2+
output = json</code></pre> +
</div> +
<p>This federation helper exchanges an OIDC token for temporary AWS credentials, and Codex reads through the standard AWS credential chain with no further configuration. Next, create or update <code>~/.codex/config.toml</code> and reference the AWS profile, see the <a href="http…+
<div class="hide-language"> +
<pre><code class="language-toml">model = "global.openai.gpt-5.6-sol"+
model_provider = "amazon-bedrock-runtime"+
model_reasoning_effort = "high"+
+
[model_providers.amazon-bedrock-runtime.aws]+
profile = "&lt;AWS_OIDC_PROFILE&gt;"+
region = "ap-southeast-2"</code></pre> +
</div> +
<p>If the helper doesn’t have a valid cached session, it opens the configured sign-in page in your browser. After you authenticate, the helper returns temporary AWS credentials through <code>credential_process</code>. Requests are signed with AWS SigV4, so no API key is involved in…+
<h2 id="quota-management">Quota management</h2> +
<p>GPT-5.6 on-demand quotas are measured in requests per minute (RPM) and tokens per minute (TPM). Token burndown determines how each request consumes TPM. Token consumption is calculated from input tokens, cache-write input tokens, and output tokens multiplied by the model’s <a href="https…+
<h2 id="monitoring-and-logging">Monitoring and logging</h2> +
<p>Because GPT-5.6 requests use the Amazon Bedrock Runtime API, requests made through the global inference profiles appear in <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-invocation-logging.html" target="_blank" rel="noopener">model invocation logging</a> lik…+
<p>CloudWatch <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-invocation-logging.html" target="_blank" rel="noopener">Coding Agent Insights</a> provides a dashboard for Codex telemetry, including token usage, API requests, active users, conversation activity, an…+
<p>The following example shows how to configure the Coding Agent Insights for Codex using the Bearer approach. First, <a href="https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/CloudWatch-OTLP-MetricsBearerTokenAuth.html" target="_blank" rel="noopener">get a CloudWatch metri…+
<div class="hide-language"> +
<pre><code class="language-toml">[otel]+
environment = "production"+
+
[otel.metrics_exporter]+
otlp-http = { endpoint = "https://monitoring.ap-southeast-2.amazonaws.com/v1/metrics", protocol = "binary", headers = { "Authorization" = "Bearer YOUR_CLOUDWATCH_METRICS_API_KEY" } }</code></pre> +
</div> +
<p>Replace <code>YOUR_CLOUDWATCH_METRICS_API_KEY</code> with the key created in CloudWatch, then start Codex. This CloudWatch metrics API key can then authorize the export to the Regional CloudWatch endpoint. After telemetry arrives, open the CloudWatch console in Asia Pacific (Syd…+
<div style="width: 810px" class="wp-caption alignnone">+
<a href="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/08/31/ML-21760-2.png" target="_blank" rel="noopener"><img title="Codex usage in CloudWatch Coding Agent Insights" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59…+
<p class="wp-caption-text">Figure 2: Codex token and request activity in CloudWatch Coding Agent Insights in Asia Pacific (Sydney)</p>+
</div> +
<p>To populate the Organization, Environment, Department, Cost Center, Location, Team, and User filters, provide the corresponding values through <code>OTEL_RESOURCE_ATTRIBUTES</code>. AWS classifies that CloudWatch metric API key as a long-term credential and recommends it only wh…+
<p>For organizations that federate developer identity through corporate single sign-on, we recommend using the enterprise rollout, where a local collector signs the export with SigV4 using the developer’s federated credentials and no token is distributed.</p> +
<h2 id="conclusion">Conclusion</h2> +
<p>In this post, we showed how to discover and invoke the GPT-5.6 Sol, Terra, and Luna global inference profiles from Asia Pacific (Sydney) and Asia Pacific (Melbourne) Regions. We also introduced how to configure Codex to use Amazon Bedrock Runtime and export Codex telemetry to CloudWatch Cod…+
<p>To get started, follow the examples in this post and test GPT-5.6 models in your account. If you are using Codex, you can configure the <a href="https://aws.amazon.com/bedrock/" target="_blank" rel="noopener">Amazon Bedrock Runtime</a> provider and enable CloudWatch Coding Agent…<hr style="width: 100%"> <h2>About the authors</h2> <footer> <div class="blog-author-box"> <div class="blog-author-image"> -
<p><img class="alignleft size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/11/ml-20969-image-6.png" alt="Dani Mitchell" width="100" height="100"></p> +
<p><img loading="lazy" class="alignleft size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/08/31/ML-21760-3.jpeg" alt="Frank Huang" width="100" height="100"></p> </div> -
<h3 class="lb-h4">Dani Mitchell</h3> -
<p>Dani is a Sr. generative AI Specialist Solutions Architect at AWS and the SA lead for Amazon Bedrock Knowledge Bases. He helps enterprises across the world design and deploy generative AI solutions using Amazon Bedrock and Anthropic’s models and capabilities to build scalable, production-…+
<h3 class="lb-h4">Frank Huang</h3> +
<p>Frank Huang, PhD, is a Senior AI/ML Specialist Solutions Architect at AWS based in Auckland, New Zealand. He focuses on helping customers deliver AI/ML solutions. Throughout his career, Frank has worked across a variety of industries such as financial services, Web3, hospitality, media an… </div> <div class="blog-author-box"> <div class="blog-author-image"> -
<p><img loading="lazy" class="alignleft size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/07/24/ML-21458-3.png" alt="Aamna Najmi" width="100" height="100"></p> +
<p><img loading="lazy" class="alignleft size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/08/31/ML-21760-4.jpg" alt="Sam Zhang" width="100" height="100"></p> </div> -
<h3 class="lb-h4">Aamna Najmi</h3> -
<p>Aamna is a Senior Specialist Solutions Architect for Generative AI focusing on Anthropic models and operationalizing and governing generative AI systems at scale on Amazon Bedrock. She helps ISVs solve their challenges, embrace innovation, and create new business opportunities with Amazon…+
<h3 class="lb-h4">Sam Zhang</h3> +
<p>Sam Zhang is a Security Specialist Technical Account Manager at AWS based in Sydney, Australia. He works with enterprises on infrastructure security, identity and access management, and threat detection, helping them build secure cloud infrastructure and workloads. His recent focus is the… </div> <div class="blog-author-box"> <div class="blog-author-image"> -
<p><img loading="lazy" class="alignleft size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/04/21/ml-20855-image-2.png" alt="Sofian Hamiti" width="100" height="100"></p> +
<p><img loading="lazy" class="alignleft size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2025/10/03/mmelli-100x133.jpg" alt="Melanie Li" width="100" height="100"></p> </div> -
<h3 class="lb-h4">Sofian Hamiti</h3> -
<p>Sofian is a technology leader with over 12 years of experience building AI solutions, and leading high-performing teams to maximize customer outcomes. He is passionate about empowering diverse talents to drive global impact and achieve their career aspirations.</p> +
<h3 class="lb-h4">Melanie Li</h3> +
<p>Melanie Li, PhD, is a Senior Generative AI Specialist Solutions Architect at AWS based in Sydney, Australia, where her focus is on working with customers to build solutions using state-of-the-art AI/ML tools. She has been actively involved in multiple generative AI initiatives across APJ,… </div> <div class="blog-author-box"> <div class="blog-author-image"> -
<p><img loading="lazy" class="alignleft size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/08/31/ML-21797-1.jpg" alt="Antonio Rodriguez" width="100" height="100"></p> +
<p><img loading="lazy" class="alignleft size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/07/22/ML-21450-4.jpg" alt="Zohreh Norouzi" width="100" height="100"></p> </div> -
<h3 class="lb-h4">Antonio Rodriguez</h3> -
<p>Antonio is a Principal Generative AI Tech Leader at Amazon Web Services. He helps companies of all sizes solve their challenges, embrace innovation, and create new business opportunities with Amazon Bedrock.</p> +
<h3 class="lb-h4">Zohreh Norouzi</h3> +
<p>Zohreh is a Senior Security Solutions Architect at Amazon Web Services (AWS). She helps customers make good security choices and accelerate their journey to the AWS Cloud. She has been actively involved in AI security initiatives, using her expertise to help customers build secure AI solu… </div> <div class="blog-author-box"> <div class="blog-author-image"> -
<p><img loading="lazy" class="alignleft size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/08/31/ML-21797-2.jpg" alt="Ayan Ray" width="100" height="100"></p> +
<p><img loading="lazy" class="alignleft size-full" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2022/06/15/Saurabh-Trikande.jpg" alt="Saurabh Trikande" width="100" height="100"></p> </div> -
<h3 class="lb-h4">Ayan Ray</h3> -
<p>Ayan is a Principal Partner Solutions Architect and AI Tech Lead at AWS, serving as the Worldwide Tech Lead for Anthropic at AWS. He works at the intersection of cloud architecture and Artificial Intelligence, helping organizations adopt and scale Anthropic’s technologies on AWS.</p>…+
<h3 class="lb-h4">Saurabh Trikande</h3> +
<p>Saurabh Trikande is a Senior Product Manager for Amazon Bedrock and Amazon SageMaker Inference. He is passionate about working with customers and partners, motivated by the goal of democratizing AI. He focuses on core challenges related to deploying complex AI applications, inference with… </div> </footer></content:encoded> @
@@ -133,251 +270,221 @@ print(next(b["text"] for b in result["content"] if b["type"] == "text"))</cod </item> <item>-
<title>From theory to delivery: How Atos upskilled 400 engineers in agentic AI</title>-
<link>https://aws.amazon.com/blogs/machine-learning/from-theory-to-delivery-how-atos-upskilled-400-engineers-in-agentic-ai/</link>+
<title>Modernizing and scaling support operations with generative AI on AWS</title>+
<link>https://aws.amazon.com/blogs/machine-learning/modernizing-and-scaling-support-operations-with-generative-ai-on-aws/</link> -
<dc:creator><![CDATA[Rajesh Babu Nuvvula]]></dc:creator>-
<pubDate>Tue, 01 Sep 2026 16:17:54 +0000</pubDate>-
<category><![CDATA[Amazon Bedrock]]></category>-
<category><![CDATA[Amazon SageMaker]]></category>+
<dc:creator><![CDATA[Carla Lorente]]></dc:creator>+
<pubDate>Wed, 02 Sep 2026 18:26:35 +0000</pubDate>+
<category><![CDATA[Advanced (300)]]></category> <category><![CDATA[Customer Solutions]]></category>-
<guid isPermaLink="false">c09782028dd4cf40d9245e4cd28716a51318e5b0</guid>+
<guid isPermaLink="false">5c6abef255152e41f7c6379eb8254ddffa13a0dc</guid>-
<description>When Atos set out to upskill 400 engineers in agentic AI, hands-on learning was the missing ingredient. Over three days, engineers built multi-agent systems on AWS through an AI League event. This post explains why Atos chose the format, what engineers built and learned, and what o…-
<content:encoded><p>When Atos set out to upskill 400 engineers from theory to delivery in agentic AI, the team faced a familiar challenge: how to build real-world capability, not only theoretical knowledge. Online courses and classroom-based instruction build foundations, but they do…-
<p>Through the <a href="https://aws.amazon.com/partners/atos/" target="_blank" rel="noopener">Atos partnership with AWS</a>, we had already seen that hands-on learning was the missing ingredient in effective AI enablement. We had previously delivered practical upskilling in reinfor…-
<p>In 2026, Atos partnered with AWS to run an agentic AI League event for 400 engineers. Over three days, engineers moved from limited hands-on experience to building multi-agent systems with pathfinding, guardrails, memory, and fine-tuned models. They competed on a live leaderboard that score…-
<p>Participant skill levels varied widely. Some were developers with existing AWS experience. Others were using AWS for the first time or held less technical roles such as product owners and project managers.</p> -
<ul> -
<li>5% had no prior knowledge of agentic AI.</li> -
<li>25% had basic awareness of the topic.</li> -
<li>50% understood the topic but had no hands-on experience.</li> -
<li>20% had practical experience with agentic AI services.</li> -
</ul> -
<p>This post explains why we chose the AI League format, what engineers built and learned, which AWS services were involved, and what other enterprises should consider when running a similar event.</p> -
<h2 id="why-the-aws-ai-league">Why the AWS AI League?</h2> -
<p>Atos has a strategic commitment to agentic AI, including the development of <a href="https://atos.net/en/services/ai-atos-sovereign-agentic-studios" target="_blank" rel="noopener">Sovereign Agentic AI Studios</a> in multiple locations worldwide. We needed a way to upskill our en…-
<p>The AI League format offered several advantages over conventional training:</p> +
<description>Learn how to build a generative AI-based support operations platform on AWS that converts training videos into structured SOPs, applies Retrieval-Augmented Generation to guide ticket resolution, and uses machine learning to predict SLA risk and prioritize work.</description>+
<content:encoded><p>Scaling support operations requires handling rising ticket volumes, meeting strict Service Level Agreements (SLAs), adapting to evolving compliance requirements, and maintaining documentation that quickly becomes outdated, all without proportional increases in hea…+
<p>To address these constraints, teams can use generative AI on AWS to capture knowledge from operational workflows, apply it during ticket resolution, and surface risks before they impact SLAs. Rather than optimizing individual tickets or documents in isolation, this approach focuses on impro…+
<h2 id="the-operational-challenge">The operational challenge</h2> +
<p>Enterprise support operations rely on process knowledge. However, as organizations grow, that knowledge becomes fragmented across documentation, people, and tools. The result is not a single failure point, but a chain of small inefficiencies that compound into slow resolution, inconsistent …+
<h3 id="documentation-exists-but-knowledge-does-not-persist">Documentation exists, but knowledge does not persist</h3> +
<p>Support teams maintain hundreds or thousands of SOPs describing how to handle requests, approvals, and system changes. However, these are typically developed on an as-needed basis for specific functions, not designed from a systems perspective. Each team documents its own slice of the proce…+
<h3 id="tickets-arrive-faster-than-guidance-can-be-found">Tickets arrive faster than guidance can be found</h3> +
<p>Every incoming ticket must be interpreted and matched to the correct procedure before work can begin. When tickets arrive faster than analysts can process them (a violation of takt time principles from Lean Six Sigma), the system accumulates backlog and delays compound. Analysts spend signi…+
<h3 id="work-is-executed-but-the-process-is-invisible">Work is executed, but the process is invisible</h3> +
<p>Even when tickets are handled successfully, teams rarely see how work actually flows across roles and systems. Some requests require multiple approvals and cross-team coordination, while others are resolved in minutes. From the outside, both appear as simple tickets. Without visibility into…+
<h3 id="priorities-are-often-identified-too-late">Priorities are often identified too late</h3> +
<p>Service targets depend on recognizing which tickets are risky before they breach deadlines. However, while priority is often automatically assigned within customer relationship management (CRM) systems based on impact and urgency, these inputs are typically assessed by analysts from ticket …+
<h3 id="leaders-see-reports-not-operations">Leaders see reports, not operations</h3> +
<p>Over time, fragmented knowledge, uneven workload, and reactive prioritization reinforce each other. Expertise concentrates in a few individuals, onboarding slows, and scaling requires adding people rather than improving process efficiency. Leaders typically rely on retrospective reporting t…+
<h2 id="solution-overview">Solution overview</h2> +
<p>To address the limitations of manual documentation, fragmented ticket handling, and reactive workload management, the solution integrates execution and analytics into a single operational system on AWS.</p> +
<p>The solution is organized into two tightly coupled layers: an operational intelligence workspace used by analysts for day-to-day work, and an analytics and decision intelligence layer used by both analysts and leaders for real-time insight and optimization. This second layer surfaces action…+
<div style="width: 810px" class="wp-caption alignnone">+
<a href="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/08/27/ML-20585-1.jpg" target="_blank" rel="noopener"><img src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/08/27/ML-20585-1.jpg" alt="High-level architecture…+
<p class="wp-caption-text">Figure 1: End-to-end architecture of the support operations solution on AWS</p>+
</div> +
<p><strong>1. Operational intelligence workspace (<a href="https://aws.amazon.com/bedrock/" target="_blank" rel="noopener">Amazon Bedrock</a> and <a href="https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/strands-sdk-memory.html" target="_blank" rel="noopener"&…Diff display stops at 400 lines. The line counts above are from the whole diff. 57 lines shown here cut at 300 characters. The raw artifact at this commit is linked above.