AWS introduces Trust in Depth framework for AI agent autonomy
AWS published a blog outlining Trust in Depth, a layered approach for giving AI agents more autonomy while retaining control. The framework has five layers: scope, know, bound, observe, and prove. It lets agents act independently when their actions can be verified, reserving human judgment for decisions that require it.
Sources and evidence
Summary last validated Oct 16, 2026
Reader actions
Report an issue
Use this for an incorrect summary, wrong source, duplicate story, or wrong category. Submissions are private and do not change the story automatically.
Related coverage
- models
Amazon Bedrock adds structured outputs for Anthropic Claude models
Amazon Bedrock now supports structured outputs for all Anthropic Claude models on both the bedrock-runtime and bedrock-mantle endpoints. The feature returns schema-compliant JSON matching a defined structure using JSON Schema output format and strict tool use, eliminating the need for custom parsing, retry logic, or post-processing.
- infrastructure
AWS opens SageMaker Studio to new users via simplified sign-up
AWS now lets new Amazon SageMaker AI customers reach a fully configured SageMaker Studio environment in a few steps. Users can sign up with an existing Amazon, Apple, GitHub, or Google identity, with no advanced AWS account or credit card required for most customers, plus $100 in AWS Free Tier credits.
- infrastructure
SageMaker JumpStart adds over 100 models from 30 providers in 2026
Amazon SageMaker JumpStart onboarded more than 100 new models from 30 providers in the first three quarters of 2026, AWS said in a year-to-date review. The recap also covers JumpStart enhancements including a simplified Model Hub, pre-configured deployment options, and Hugging Face deep link integration.
- models
TwelveLabs Pegasus 1.5 video model launches in Amazon Bedrock
TwelveLabs' Pegasus 1.5 is now available in Amazon Bedrock. The video language model converts long-form video into structured, timestamped JSON metadata by matching user-defined moments. New features include video segmentation, multimodal prompting with reference images, and on-the-fly processing of up to two hours of video.
- infrastructure
Deepgram ports Nova-3 speech-to-text to AWS Trainium2 for batch inference
Deepgram worked with the AWS Forward Deployed Engineering team to port its Nova-3 speech-to-text model to AWS Trainium2 for offline batch inference. The work covered porting to the AWS Neuron stack, kernel-level tuning, and a data-parallel Rust runtime, with reported throughput and cost results for batch transcription.