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7 diapositives35:19EN
AI Engineer· 25 oct. 2026SallyAnn 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.Évaluations et observabilitéÉvaluationsIngénierie d’agents+2
8 diapositives15:27EN
AI Engineer· 25 oct. 2026What 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.Ingénierie d’agentsÉvaluationsObservabilité et fiabilité+2
5 diapositives34:11EN
GOTO Conferences· 23 oct. 2026Anatoliy Bilenko and Alberto Carretero: Making Database Protocols Reliable with Formal Methods and AIAnatoliy Bilenko, Alberto Carretero · CanonicalCanonical’s database engineers connect executable specifications, model-based tests, failure exploration, and reconstructed execution histories—and explain why none of these techniques proves a real system correct on its own.Workflows de développement et testsCode review & verificationObservabilité et fiabilité+2
53:51EN
JSJane Street· 7 oct. 2024Liora Friedberg on Solving Puzzles in ProductionYaron Minsky, Liora Friedberg · Jane StreetProduction engineering combines live incident response with software, process improvements and a culture that helps people learn from mistakes. Liora Friedberg and Ron Minsky explore the work at Jane Street.Observabilité et fiabilitéSoftware engineeringWorkflows de développement et tests