AWS announced general availability of training sessions within Amazon SageMaker AI serverless model customization. Teams can write their own post-training loops — including custom loss functions and reward signals for reinforcement learning — while SageMaker AI handles GPU allocation, distributed execution, fault recovery, and checkpoint storage. Supported techniques include SFT, DPO, RLVR, RLAIF, and multi-turn RL, and users can choose LoRA or full fine-tuning. The feature supports models including gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B, and Qwen3.8-27B, and billing is based on tokens processed, pausing while a user's reward function scores responses.
AWS published a walkthrough for the Adaptive Agentic Intelligence Framework (AAIF), built on Strands Agents, which routes simple requests to small local models (via Ollama) and only sends complex requests to cloud models through Amazon Bedrock. A lightweight 1B-parameter model (llama3.2:1b) acts as an orchestrator deciding where each request should go, with an example IoT monitoring agent that uses local telemetry retrieval, local vision analysis, and cloud-based expert insights. AWS states that in the hybrid setup, roughly 80 percent of traffic can be handled locally without hitting a paid API, and the architecture can run fully on edge hardware (e.g. Raspberry Pi with 8GB memory), fully in the cloud, or in a hybrid mode.
AWS introduced Agent Registry, a managed catalog service for Model Context Protocol (MCP) servers, agents, and agent skills. Instead of hardcoding every tool into an orchestrator, agents can now search an approved, versioned catalog at runtime and build a client from the returned descriptor. A reference sample demonstrates a Discover → Instantiate → Execute loop using Amazon Bedrock AgentCore Gateway and AgentCore runtime, with Anthropic Claude Sonnet as the foundation model.
Eon, a data protection ISV running on AWS, replaced per-tool credentials and local MCP servers with a single Amazon Bedrock AgentCore MCP Gateway. The gateway uses Cedar-based policies to enforce least-privilege access for both human engineers and an autonomous SRE agent running Claude on Amazon Bedrock, routing calls to MCP servers hosted on AgentCore Runtimes across multiple AWS accounts. Developer onboarding time dropped from about two days to about two minutes after rolling the system out to 100+ developers and roughly 10,000 sessions.
AWS and Keyfactor published a reference design that fixes a known weak point in Model Context Protocol (MCP) deployments: MCP has no built-in way to authenticate which agent is actually making a request. The architecture adds three trust layers — user-to-system, agent-to-agent, and agent-to-tool-server — built on signed request envelopes, delegation assertions signed by the user, RFC 8693 audience-restricted tokens, and live OCSP revocation checks. A working example runs on Amazon Bedrock and the AWS Strands Agents SDK with Keyfactor EJBCA issuing and validating the X.509 certificates, and the full code is on GitHub.
AWS has rolled out structured outputs across every Anthropic Claude model hosted on Amazon Bedrock, reaching both the standard runtime endpoint and the newer Mantle endpoint built for agentic workloads. Instead of hoping the model returns valid JSON, developers can now lock responses to an exact schema through constrained decoding, which blocks the model from generating tokens that would break the format. A companion feature, strict tool use, applies the same guarantee to function-calling arguments so agent workflows don't fail on malformed inputs.
AWS published a governance framework called Trust in Depth, designed to let AI agents operate with more autonomy while staying within enforceable limits. It combines scoping agent permissions before deployment, giving each agent a verifiable identity, enforcing boundaries at runtime via policy gateways, and observing agent behavior afterward. The framework leans on automated reasoning (mathematical proof) for rules that can be precisely defined, and human judgment for ambiguous cases, using AWS tools like Bedrock AgentCore, Guardrails, Kiro, Strands, Agent Registry and CloudWatch Omni.
Orca Security built Discovery AI, an Amazon Bedrock-based agent that turns plain-English security questions into structured queries against its Sonar system, which tracks over 900 cloud resource types. Rather than relying on one large model with the full schema stuffed into every prompt, the production version splits the work across nine specialized stages and multiple foundation models. The result: dramatically lower cost, faster responses, and better accuracy on complex queries, now running live since March 2026.
AWS published a joint engineering write-up with Postman describing how Agent Mode, Postman's AI-native interface for API testing and documentation, was built to run in production at scale. Rather than model quality, the team found that tool sprawl and missing context were the biggest obstacles to reliable agent behavior. The post details architectural patterns including dynamic tool scoping, schema-based query access, dedicated context handlers, and how Amazon Bedrock provides model flexibility, cross-Region inference, data residency controls, and prompt caching.
AWS released a step-by-step walkthrough showing how to give Claude Desktop (on Amazon Bedrock) live internet access through Amazon Bedrock AgentCore Gateway, closing the gap left by the model's training cutoff. The setup wires AWS IAM Identity Center SSO through Amazon Cognito as a SAML-to-OAuth federation layer, issuing JWTs that the AgentCore Gateway validates on every request. The Web Search capability itself is MCP-compatible and runs on an internal Amazon web index covering tens of billions of documents, so no query traffic leaves AWS and no third-party API key is needed. The feature is currently limited to three AWS Regions: us-east-1 (N. Virginia), eu-west-1 (Ireland), and ap-northeast-1 (Tokyo).