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Architecting the Data Layer for AI Agents: from Transactional Systems to MCP and Semantic Models
Fabiane Nardon explains how to architect enterprise data platforms for AI agents, balancing precision, security, and token costs using MCP, data mesh, and semantic web technologies.
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SafeChat: Building AI-Powered Safety Systems at Scale in a Real-Time Marketplace
Bruna Pereira shares how DoorDash built a scalable AI moderation platform. Learn how combining cheap classifiers with LLM scoring reduced incidents and cut latency in real-time chat.
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From Fab To Token - The State Of The Market
Jordan Nanos explains how hardware constraints, data center scale, and chip co-design shape modern AI performance and tokenomics from silicon to inference.
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From Thousands to One: Building LLM-Powered Selection Systems
Jendrik Jördening explains how to build reliable LLM architecture by enforcing strict schemas, separating AI text reasoning from deterministic code, and applying automated output validation.
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From Models to Agents: Building Context-Aware Consumer AI at Scale at DoorDash
Sudeep Das explains how DoorDash transforms e-commerce recommendations using LLM-driven semantic consumer memory, hierarchical semantic IDs, grounded agentic search, and multi-tiered LLM ranking.
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Architecting AI Systems for the Messy Reality of Enterprises: Why Agentic Compute is the Missing Layer
Arun Joseph explains how to successfully build and deploy enterprise-grade agentic platforms by leveraging existing teams, JVM stacks, and a "compute as the agent" architectural paradigm.
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From Copy-Paste to Composition: Building Agents Like Real Software
Jake Mannix explains how to mature AI agents using "virtual tools" for encapsulation, interface abstraction, and deterministic taint tracking to prevent lethal data exfiltration risks.
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