A panel of university students from LSE, Princeton, Berkeley, and ASU discuss the real-world impact of AI on campus life. They cover how AI is used as both a powerful learning tool and a crutch, the innovative projects students are building, how universities are adapting, and the challenges of navigating cheating, job applications, and 'AI slop' in a rapidly changing educational landscape.
Explore how Temporal, a durable execution framework, brings resilience and scalability to AI agents built with the OpenAI Agents SDK. This summary covers Temporal's core concepts of Workflows and Activities, the official integration that makes OpenAI agents durable, and patterns for orchestrating multiple micro-agents.
This talk introduces 'structured dissent,' a multi-agent orchestration pattern where believer, skeptic, and neutral agents debate decisions to overcome the 'confidently wrong' failure mode of single-agent LLM systems, improving reliability for high-stakes tasks like cybersecurity analysis.
A deep dive into the security vulnerabilities of Multi-Context Protocol (MCP) for AI agents. The talk explores how identity loss, "all-or-nothing" permissions, and disappearing audit trails create significant attack surfaces, and presents solutions like identity chain tracking, context-aware permissions, and intelligent auditing to secure agent-to-tool communication.
A detailed overview of a modular, five-agent system designed to combat the entire lifecycle of digital misinformation. Based on an ICWSM research paper, this practitioner's guide details the roles of the Classifier, Indexer, Extractor, Corrector, and Verifier agents. The system emphasizes scalability, explainability, and high precision, moving beyond the limitations of single-LLM solutions. The talk covers the complete blueprint, from agent coordination and MLOps to holistic evaluation and optimization strategies for production environments.
Panos Stravopodis, CTO of Elyos AI, shares the infrastructure and orchestration challenges of building production-ready voice AI agents. He details the four pillars for success—latency, consistency, context, and recovery—and provides engineering patterns for error handling, context management, and achieving conversational coherence in real-time systems.