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Search: “trusted execution environment”
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Synthesized builds Test Data Agent to validate AI agents with production-like data
Synthesized announced a Test Data Agent that provisions production-like data and system states to validate enterprise AI agents before deployment.
Synthesized unveiled its Test Data Agent, an agentic infrastructure capability that generates, masks, and subsets production-representative data for testing AI agents under realistic enterprise conditions. It integrates with agent development, evaluation, testing, and orchestration frameworks, with purpose-built support for complex SAP estates including finance, procurement, and supply-chain workflows and ECC-to-S/4HANA transformation programs. The product runs in on-premises, private-cloud, and hybrid environments and exposes REST APIs and CI/CD triggers for repeatable validation scenarios.
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Linux Foundation takes on TRACE, a hardware-backed runtime evidence specification for AI agents
The Linux Foundation adopts TRACE, an OPAQUE-contributed spec giving AI agents hardware-attested, cryptographically verifiable runtime and compliance evidence.
The Linux Foundation accepted the TRACE (Trust, Runtime Attestation and Compliance Evidence) specification contributed by OPAQUE, developed with AMD, Intel, Microsoft, and the Technology Innovation Institute. TRACE binds runtime environment, software, policies, data classifications, and tool usage into a portable, cryptographically verifiable artifact, composing existing standards such as RATS, EAT, SLSA, SCITT, SPIFFE, and EAR. It recorded nearly 135,000 PyPI downloads within 10 weeks of its June 2026 introduction, and its technical workstream will be hosted by the Coalition for Secure AI.
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Agent-net Open Sources Webagent: A Go Harness That Turns Any Website into a Guarded AI Agent
Agent-net open-sourced Webagent, a Go harness turning websites into AI agents with code-enforced guardrails wrapping every tool call.
Agent-net released Webagent under Apache 2.0, a Go framework where a business fills in a declarative JSON spec, picks one provider for each of nine pluggable slots (retrieval, memory, guardrail, channel, secrets, presenter, model, action, observability), and runs webagent serve. Every tool the agent holds is wrapped by action.Guard so the chosen guardrail executes before any action runs and the model cannot bypass it. Live capabilities include OpenRouter/gateway LLM brains, MCP tools over Streamable HTTP, and Slack, WhatsApp, and HTTP channels; browser actions, OAuth-gated MCP, OTel export, and AgentNet identity/billing are not yet built. The project is v0 with a deferred-hardening list and cites arXiv 2511.19477 on an 85% versus 50% task-success gap attributed to architecture over model capability.
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Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend
MarkTechPost tutorial walks through NVIDIA's cuDNN Frontend graph API, covering kernel fusion, autotuning, plan reuse, and CUDA graph capture on Colab GPUs.
The tutorial explains how to express GPU computations as operation graphs via the cuDNN Frontend graph API, running the five-step build pipeline of validate, build operation graph, create execution plans, check support, and build plans. It progresses from a single fused convolution with bias and ReLU to autotuning across engine configs, FP8-style epilogues, attention, plan serialization, dynamic shapes, and CUDA graph capture. Each kernel is benchmarked against a PyTorch reference on a single Colab GPU to verify correctness and measure cost. The piece also covers practical setup issues like making libcudnn.so visible to the frontend's dynamic loader.
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