MCP is all you need — Samuel Colvin, Pydantic
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スライド 12 枚17:56EN
AI Engineer· 2026年10月25日How Bridgewater Keeps Its AI Analyst Inside Permission BoundariesNikhil Benesch, Aaron Linsky · turbopufferAaron Linsky and Nikhil Benesch explain why search over an enterprise vault must honor document permissions without sacrificing recall or speed. Their solution combines vector indexing with native filtering to search hundreds of millions of vectors in roughly 120 milliseconds.エンタープライズ AIPrivacy & data governanceSearch & retrieval+2
スライド 7 枚35:19ENAI Engineer· 2026年10月25日SallyAnn DeLucia: How to Write Your First AI EvalSallyAnn DeLucia · Arize AITurn an agent’s real traces into concrete pass/fail criteria, then build an LLM judge you can test, align with people, and run in production.評価とオブザーバビリティ評価（Evals）エージェント工学+2 
スライド 9 枚20:58ENAI Engineer· 2026年10月25日Vinoo Ganesh on Making Financial AI Cite Every NumberVinoo Ganesh · KeplerKepler CEO Vinoo Ganesh explains why financial AI should retrieve and cite figures while deterministic software handles the math. His design makes traceability, retrieval quality, and tenant security part of the answer itself.金融 AIエンタープライズ AI検索拡張生成（RAG）+2 
スライド 10 枚18:31ENAI Engineer· 2026年10月25日Janie Gu and Suyeol Yun: Why Frontier Models Fail as Buy-Side AnalystsJanie Gu, Suyeol Yun · Google DeepMindJanie Gu explains why benchmark scores can mislead, while Suyeol Yun presents MandateBench, which tests whether a model can turn a fund mandate into a defensible investment memo. The two talks show why real workflows, reproducible grading, and evidence-backed reasoning matter.金融 AIBenchmarks評価（Evals）+2 
スライド 8 枚15:27ENAI Engineer· 2026年10月25日What AI Engineers Do Now: Build the Loop Around AgentsAparna Dhinakaran · Arize AIAparna Dhinakaran argues that AI engineers are moving from shipping individual features to building systems that let agent teams improve them. At Arize, that means tracing agent behavior, evaluating its work against human judgment, and feeding those signals back into an always-on agent.エージェント工学評価（Evals）オブザーバビリティと信頼性+2 
スライド 7 枚19:37ENAI Engineer· 2026年10月25日Jay Sahnan and Arik Bird on Building Financial Agents for the Whole WebJay Sahnan, Arik Bird · BrowserbaseBrowserbase’s Jay Sahnan and Arik Bird show how browser agents reach financial data hidden behind logins and bot defenses. Their Ramp, Bill, and Stripe examples connect reliable cloud browsers with Stagehand’s more adaptive controls.エージェント工学Computer useWorkflow automation+2