Skip to content
Saturday, 24 October 2026
Tenesys AI News
Subscribe

AI Agents

AI agents, MCP, tool and function calling, coding and browser agents, multi-agent systems.

All NewsModelsAgentsVoice AIAutomationBusinessResearchCyber Security
Any timeTodayThis WeekThis Month🔴 Breaking🟠 Important🧪 Worth Testing

“AWS” — 4 articles ✕

Agents· Important· 🧪 Worth Testing

AWS launches general availability of SageMaker AI training sessions for custom model fine-tuning

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 Machine Learning
Agents· Important· 🧪 Worth Testing

AWS launches Agent Registry for runtime discovery of MCP tools and agents

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.

AWS Machine Learning
Agents· Important· 🧪 Worth Testing

AWS and Keyfactor show how to lock down multi-agent identity using certificates instead of shared tokens

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 Machine Learning
Agents· Important· 🧪 Worth Testing

Amazon Bedrock Enforces Schema-Accurate JSON for All Claude Models

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 Machine Learning