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Based on Antidoom released by Liquid AI, this is a practical guide that explains how to detect a doom loop in an inference model, correct only the loop start token with FTPO, and verify it with operational indicators.
We explain Vercel AI SDK 7 as an agent runtime design tool, not as a simple model call SDK. We have compiled a practical guide on when to introduce runtime context, tool authorization, WorkflowAgent, HarnessAgent, and Gateway fallback.
Huawei released data on Kirin 2026's integration and power efficiency improvement in the same manufacturing process. This issue is explained not as a debate over EUV replacement, but as a verification issue for optimization of the same process.
We summarize in a practical flow how the approval queue, policy engine, human inbox, and idempotent executor should be divided before the AI agent executes external actions.
The suspension of new Amazon Mechanical Turk signups is not the end of human labeling, but rather shows the limitations of human data without provenance and logs of activity. The AI learning and evaluation data team has compiled criteria for redesigning the labeling supply chain.
The Agent Control Specification and ASSERT, released at Microsoft Build 2026, signal a shift in agent control from prompted advice to runtime policy, audit, and regression evaluation. This article summarizes the actual introduction sequence and failure prevention criteria.
The KAIST HPCA 2026 study shows that AI agents can spend up to 136.5 times more energy per request than simple chatbots. Before releasing the agent feature, the development team compiled a rundown of how we should measure repeated calls, tool waits, GPU idle time, and Wh per request.
The GA of managed-settings.json on GitHub Enterprise signals the move of Copilot from a personal tool to the enterprise operations layer. This article organizes model defaults, plugin marketplace, YOLO mode blocking, and verification loop into practical terms.
Amazon's on-device AI chip and screen-less AI device preview were explained from the perspective of smart home and wearable product design. Local inference, sensor permissions, cloud fallback, and completion standards were organized into standards that the actual product team could immediately check.