Atlassian's Tamar Yehoshua explains what the AI builder era looks like for product managers in a company of more than 10,000 people. Using Confluence, RovoClaw, and Jira as examples, she covers when PMs should write code and prototypes, when they should steer the team instead, and how Atlassian is building AI fluency in its PM organization.
This Build Hour focuses on "value maxing" with GPT-5.6, shifting from simply tracking token usage to measuring the actual outcomes and efficiency gained from AI. It covers how to select the right GPT-5.6 model (Sol, Terra, Luna) based on intelligence, latency, and cost, and provides practical strategies for optimizing cost-performance. Key topics include leveraging programmatic tool calling, prompt caching, persistent reasoning, and context compaction for API users, along with CodeX-specific tips. A customer spotlight on Ploy demonstrates real-world application, showcasing their migration to GPT-5.6 Sol, which resulted in 2.2x faster builds at 27% lower cost through advanced caching and tool optimization techniques.
An evaluation platform is more than a simple test runner; it's a complex system for creating shared definitions of quality. This talk explores the evolution of eval platforms from basic spreadsheets to sophisticated, integrated systems, highlighting the hidden data and systems engineering challenges involved in making them credible, scalable, and usable for building trustworthy AI agents.
Aishwarya Naresh Reganti and Kiriti Badam, with experience from OpenAI, Google, and Amazon, share a framework for building successful enterprise AI products. They detail why AI development differs from traditional software, emphasizing the challenges of non-determinism and the agency-control trade-off, and introduce their 'Continuous Calibration, Continuous Development' (CC/CD) lifecycle to build reliable, value-driven AI systems.
A deep dive into OpenAI's AgentKit, demonstrating how to visually build, deploy, and optimize multi-step, tool-calling agents using Agent Builder, ChatKit, and the integrated Evals platform.
Chip Huyen, an AI expert and author of 'AI Engineering', explains the realities of building successful AI applications. She covers the nuances of model training, the critical role of data quality in RAG systems, the mechanics of RLHF, and why the future of AI improvement lies in post-training, system-level thinking, and solving UX problems rather than just chasing the newest models.