The agentic launches that stayed with me last week had nothing to do with how capable a model is. They were about reach. AWS Step Functions , which already orchestrates over 220 AWS services, can now be handed to an agent as a tool, so workflows teams already trust became callable without a rewrite.
In the same week, the AI blog published how one engagement platform exposed roughly 400 internal API operations the same way. Neither story involves rebuilding anything, only describing what exists and letting the agent find it. The other thread was cost.
OpenAI’s newest Terra model reached Amazon Bedrock priced well below the model beside it, and two post-quantum launches landed for a deadline nobody can put on a calendar yet. Now, let’s get into this week’s AWS news… Headlines OpenAI GPT-6. 1 Terra is now generally available on Amazon Bedrock , and what interests me is where it sits rather than what it tops.
Terra is the everyday-workload line of the GPT-6 family. In OpenAI’s Agents’ Last Exam evaluation it scored 15 percentage points higher than GPT-5. 6 Terra, performed on par with GPT-6 Astra, and completed tasks more than three times faster than both.
Those are OpenAI’s figures, not an independent test. The price is what I would check first: input and output tokens are at least 50% lower than GPT-6. 1 Sol and GPT-5.
6 Terra. It takes up to 1 million input tokens with text and image input, and you can reach it from the Bedrock APIs or ChatGPT Work and Codex. AWS Step Functions workflows are now available as AI agent tools through Amazon Bedrock AgentCore Gateway, which connects agents to tools over the Model Context Protocol (MCP).
The division of labor is the idea: the agent decides when to run a workflow and what input to pass; Step Functions runs each step exactly as you defined it. A support agent can trigger a refund, and the eligibility check, the manager approval, and the credit still happen in that order every time, because the ordering was never the model’s to improvise. Your existing Standard and Express workflows qualify as connector targets, and agents can also look up runs.
It’s available in all Regions where AgentCore Gateway is, at standard rates. Last week’s launches Here are some launches and updates from this past week that caught my attention: AWS Key Management Service now supports ML-KEM for post-quantum key encapsulation – You can now establish a shared secret with a post-quantum algorithm using AWS KMS keys, with private keys in FIPS 140-3 Level 3 validated HSMs.
All three NIST FIPS 203 parameter sets are supported through Encapsulate , which produces a random 32-byte shared secret and its ciphertext, and Decapsulate , which recovers it. The threat is harvest now, decrypt later: an adversary records today’s key exchange and recovers the secret once quantum computers are powerful enough. It’s available everywhere KMS runs except the Middle East (UAE and Bahrain) and China Regions.
Amazon Aurora PostgreSQL added post-quantum TLS key exchange the same week, on versions 18 and higher. AWS IAM Identity Center now supports cross-Region trusted identity propagation – You can now carry a user’s identity with their request to AWS services in another Region. This removes a genuine architectural constraint: trusted identity propagation used to require every service accessing that identity to sit in the same Region, which left two unappealing options, broad IAM roles that cannot distinguish individual users, or consolidating all your services into a single Region.
Now the identity travels with the request automatically, so you grant access by user and group membership and audit who accessed what. It’s available at no additional cost everywhere IAM Identity Center runs. Amazon ECS now supports GPU sharing with NVIDIA Multi-Process Service (MPS) – You can now run multiple Amazon ECS tasks concurrently on a single NVIDIA GPU, the practical answer to work that only ever uses a fraction of one, like small-model inference and multi-model serving.
You declare the GPU memory each container needs and can optionally cap its share of GPU compute, and ECS places tasks on GPUs with available capacity as your services scale, with no code changes. It works on ECS Managed Instances and ECS on Amazon EC2 , and on Managed Instances it watches the MPS control daemon and replaces the instance if it turns unhealthy.
It’s available in all commercial and AWS GovCloud (US) Regions. AWS Lambda supports Seekable OCI (SOCI) lazy loading for Lambda Managed Instances – You can now have AWS Lambda fetch only the files needed at each stage of startup instead of pulling a whole container image first, which AWS puts at up to 70% off container startup time. You create a SOCI index, push it to Amazon ECR , point the image URI at the indexed tag, and Lambda detects it automatically.
The technique also reached Amazon SageMaker AI training and processing jobs, with benchmarks : lazy loading held at 3 to 11 seconds regardless of image size, against a median 3 minutes 42 seconds for a 13. 2 GB image, a range they report as 8x to 46x. Amazon EKS Auto Mode now supports Kubernetes Gateway API – You can now declare how traffic reaches your applications with Gateway and HTTPRoute resources on Amazon EKS without installing a separate controller, and EKS Auto Mode provisions and manages the underlying AWS load balancing infrastructure.
You get HTTP and gRPC routing, cross-namespace routing, weighted traffic splitting, header-based routing, and automatic TLS certificate discovery through AWS Certificate Manager . Existing Ingress workloads keep working alongside them, so this is additive rather than a migration. Gateway API support is included with EKS Auto Mode; standard load balancing charges apply.
For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.
Originally published at aws.amazon.com
