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Wednesday

  2. Alibaba Cloud Developer Community AI

    After an AI Agent fails acceptance: how do review comments feed into the next round of guidance?

    In the default path of AI Tutor Engine, when a student already has a guidance session and continues to ask questions on the same task after submitting for acceptance, the detailed review comments are not automatically loaded into the next model request; the model can only see a status and score summary. The author used unique markers to inspect requests, verified two reachable paths—initial full-history initialization and the student actively pasting the comments—and proposed a feedback contract that selects feedback in submission and review order, limits the context budget, and updates outdated comments. This work did not complete a production fix, nor did it evaluate the guidance effect of a real model.

    In the default path of AI Tutor Engine, when a student already has a guidance session and continues to ask questions on the same task after submitting for acceptance, the detailed review comments are not automatically loaded into the next model request; the model can only see a status and score summary. The author used unique markers to inspect requests, verified two reachable paths—initial full-history initialization and the student actively pasting the comments—and proposed a feedback contract

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    #hot-signal#Tutorials / practice#Agents#English text is being prepared.
  4. The New Stack

    Nvidia adds Agent Knowledge Foundation to Agent Toolkit

    At GTC Berlin, Nvidia added Agent Knowledge Foundation to Agent Toolkit, packaging the Nemotron Retriever models, the new Auto-Ontology library, the Kumo family of models for structured data, and CUDA-X data processing libraries; data remains in place and is managed under enterprises' existing permissions. Nvidia says Nemotron Retriever improved search relevance by 65% in tests in the open-source OpenSearch environment, SAP's fine-tuned reranker runs nearly three times faster, and ServiceNow's reranking delivers better quality with latency reduced by nearly 25%. Auto-Ontology provides a REST API and an MCP server for agents to query, and Nvidia says it does not compete with data platforms such as Snowflake but rather partners with them.

    At GTC Berlin, Nvidia added Agent Knowledge Foundation to Agent Toolkit, packaging the Nemotron Retriever models, the new Auto-Ontology library, the Kumo family of models for structured data, and CUDA-X data processing libraries; data remains in place and is managed under enterprises' existing permissions. Nvidia says Nemotron Retriever improved search relevance by 65% in tests in the open-source OpenSearch environment, SAP's fine-tuned reranker runs nearly three times faster, and ServiceNow's r

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    #hot-signal#Product updates#Agents#RAG
  5. Analytics India Magazine

    Microsoft proposes using Guardian Agents to supervise enterprise AI agents

    Hayete Gallot, Executive Vice President of Microsoft's security division, proposed in a blog post deploying Guardian Agents to monitor and intervene before AI agents execute actions, and stated that frontier models cannot be trusted regardless of their origin. The plan intends to bring enterprise agents under centralized management, assigning each agent an identity, container, and permission policy, then combining identity, data, cloud, endpoint, network, and runtime signals to assess risk in real time, with red team and blue team agents intervening when risk rises. The plan will be implemented on the basis of Microsoft's Project Perception security system announced in July.

    Hayete Gallot, Executive Vice President of Microsoft's security division, proposed in a blog post deploying Guardian Agents to monitor and intervene before AI agents execute actions, and stated that frontier models cannot be trusted regardless of their origin. The plan intends to bring enterprise agents under centralized management, assigning each agent an identity, container, and permission policy, then combining identity, data, cloud, endpoint, network, and runtime signals to assess risk in re

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    #hot-signal#Safety / alignment#Agents#Microsoft
  13. Arena

    Meta Muse Wonder Full (Max) rises to #7 in Agent Arena

    Meta's Muse Wonder Full (Max) ranks #7 in Agent Arena with a net gain of +11.3%, covering over 6.5K real agent sessions and helping Meta rise to #4 among labs. The model is 7.5 percentage points higher than Muse Wonder (Max) and 7.8 percentage points higher than Muse Spark 1.3 (Max), with category rankings of #5 in Chat, #6 in Code, and #7 in Work. At the signal level, it ranks #6 in Praise vs. Complaint (+21.9%), #6 in Confirmed Success (+13.4%), #7 in Steerability (+11.2%), and #16 in Bash Reliability (+7.7%), with no issues in Tool Hallucination.

    Meta's Muse Wonder Full (Max) ranks 7 in Agent Arena with a net gain of +11.3%, covering over 6.5K real agent sessions and helping Meta rise to 4 among labs. The model is 7.5 percentage points higher than Muse Wonder (Max) and 7.8 percentage points higher than Muse Spark 1.3 (Max), with category rankings of 5 in Chat, 6 in Code, and 7 in Work. At the signal level, it ranks 6 in Praise vs. Complaint (+21.9%), 6 in Confirmed Success (+13.4%), 7 in Steerability (+11.2%), and 16 in Bash Reliability

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    #editorial#Evaluations / benchmarks#Agents#Meta
  15. AWS Machine Learning

    From Data to Decisions: Building Conversational Supply Chain Intelligence with Amazon Quick and Amazon Bedrock AgentCore

    The combination of Amazon Quick and Amazon Bedrock AgentCore turns supply chain data from systems such as ERP, monitoring, and procurement into a conversational MCP tool layer, so users can get cross-system answers in natural language. In the example, a TechComp production stoppage event is assessed via assess_disruption_impact, which finds a $750K risk, 23 orders, and three days of inventory, and recommend_mitigation then chains together governance and quoting tools to generate an emergency RFQ. All changes require approval from designated personnel, and the same workflow can also be triggered to run automatically on a schedule or by email.

    Diagram showing domain expertise with business users while operational data is split across ERP, monitoring, procurement, and email systems

    The combination of Amazon Quick and Amazon Bedrock AgentCore turns supply chain data from systems such as ERP, monitoring, and procurement into a conversational MCP tool layer, so users can get cross-system answers in natural language. In the example, a TechComp production stoppage event is assessed via assess disruption impact, which finds a $750K risk, 23 orders, and three days of inventory, and recommend mitigation then chains together governance and quoting tools to generate an emergency RFQ

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    #lab#Tutorials / practice#Agents#English text is being prepared.

Tuesday

  1. ElevenLabs Blog

    Trinity Logistics automates inbound calls with ElevenAgents, boosting profit margin by 25%

    Trinity Logistics moved its inbound sales process to ElevenLabs' ElevenAgents, automating carrier quote requests, negotiation, and booking. Automated freight profit margins are 25% higher than with the old system, and platform costs per minute are 40% lower. The system handles about 30,000 inbound calls per month, automatically books 1,000 freight loads, matches the old system's 4% successful booking rate, and handles about 60% to 70% of carrier calls.

    Jessica Day Headshot

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    #company#Industry news#Speech#Agents
  4. Philipp Schmid

    Gemini Managed Agents supports running Gemini 4 Argon agents with a single API call

    Philipp Schmid explains that the easiest way to try Gemini 4 Argon in an agent environment is Gemini Managed Agents, where a single API call provides Python, Node.js, Git, and Bash, along with full network access and control, a dedicated Credentials API, a file system for persistent work, and background support. The tweet includes a link.

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    #editorial#Product updates#Agents#English text is being prepared.