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.
Hitachi's Financial Business Unit developed a specialized LLM to automate the review of system design documents, addressing the inadequacy of general-purpose AI for mission-critical systems. This presentation details the model's development using Continued Pre-training and LoRA on proprietary data, its integration into a multi-agent architecture, and the use of Weights & Biases for MLOps, which led to a 70% reduction in manual review workload.
This is a step-by-step walkthrough for onboarding to OpenAI Codex. It covers installing the CLI and VS Code extension, configuring your workflow with agents.md and config.toml, and applying effective prompting patterns. The session also dives into advanced use cases, including integrating external tools via MCPs, running Codex programmatically in headless mode, and building multi-agent systems with the Agents SDK.
A step-by-step walkthrough on getting started with OpenAI's Codex. This guide covers installation of the CLI and VS Code extension, configuration using `agents.md` and `config.toml`, effective prompting patterns, and advanced workflows like using the Model-Connectable Protocol (MCP) and the OpenAI Agents SDK for programmatic automation.
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.
Drawing from their experience at Liberty Mutual, a Fortune 100 company, Garth Gilmour and Stuart Greenlees share nine hard-won lessons from deploying Generative AI for 5,000 developers. This "from the trenches" talk moves beyond the hype to discuss the real-world challenges of scaling AI, including managing spiraling costs, the complexities of RAG, the difference between shipping and adoption, and the necessity of building a governed platform. They detail their mistakes, solutions, and the evolution of their strategy for architecture, developer education, and model management.