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슬라이드 5장20:55EN
AI Engineer· 2026년 10월 26일Jesse Hall:语音智能体要为对话而优化,而非只看排行榜Jesse Hall · LiveKitLiveKit 的 Jesse Hall 解释了为什么语音智能体必须像实时系统一样接受评测:听清用户、允许打断、在工具调用期间继续对话,并在用户真正听到声音之前控制延迟。음성 AI에이전트 엔지니어링평가(Evals)+2
슬라이드 7장35:19EN
AI Engineer· 2026년 10월 25일SallyAnn 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.평가와 관측 가능성평가(Evals)에이전트 엔지니어링+2
슬라이드 10장18:31EN
AI Engineer· 2026년 10월 25일Janie 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.금융 AIBenchmarks평가(Evals)+2
슬라이드 8장15:27EN
AI Engineer· 2026년 10월 25일What 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.에이전트 엔지니어링평가(Evals)관측 가능성과 안정성+2
슬라이드 2장17:42EN
AI Engineer· 2026년 10월 21일Is a Faster Model Actually Faster? Measure the Finished Job, Not the TokensMorgan Suo · MiniMaxMiniMax's Morgan Suo takes the engineering changes inside M3.1 Flash one by one and asks the question that matters to users: does the job an analyst needs actually get done sooner, cheaper and correctly?AI cost & performanceInferenceQuantization+2