Charlie Holtz, co-founder and CEO of Conductor, shares six principles he has observed among the best AI-assisted builders, including staying near the frontier, creating slop-free zones, and giving agents cloud sandboxes. He also demos real-time collaboration and phone-initiated agents, and argues for orchestras over factories, with humans at the center of AI-driven development.
Erina Karati and Arunachalam Manikandan describe Project Paradox, a Supercell framework for game agents with per-agent memory, emotions and trust scores, and how they wrapped it in an autoresearch loop to fix long-horizon failures such as lost rumor sources and hardened uncertainty. The talk covers controlled scenario suites, a balanced scorecard, a small frozen policy surface, rollback discipline, and lessons applicable to support, research, coding and personal assistant agents.
Noam Brown discusses OpenAI's multi-agent system, which solved a Millennium Prize Problem using 10,000 agents. He explores the implications of this massive parallelization for AI capabilities, the rapid progress in mathematics, and the accelerating pace of AI development. A significant portion of the conversation is dedicated to the critical challenges of AI alignment, particularly in light of incidents like the 'Hugging Face' event, and how to ensure models remain aligned during Recursive Self-Improvement (RSI) as their capabilities and operational horizons expand rapidly.
A deep dive into the evolving world of LLM harnesses, demonstrating how they've transformed from simple prompt engineering into sophisticated self-improving systems. The discussion covers architectural advancements (e.g., Prime Agent's context as cache, Turing vs. Von Neumann), practical applications (auto-researchers, on-device personal AI like OpenJarvis, YC's QM for work), and the challenges and future directions of agentic AI. It emphasizes that harness innovation is key to unlocking significant performance gains and enabling LLMs to operate autonomously and adaptively.
Roberto Milev and Uday Kanagala from Navan discuss the challenges and emerging best practices in building and operating AI agents, drawing parallels with microservices. They highlight the shift to stateful runtimes, innovative memory and context management using 'skills,' and crucial solutions for observability and testing non-deterministic systems. A central theme is the critical need for robust authorization and guardrails, given the blurring line of agentic action, and a preference for mastering single-agent loops before scaling to complex orchestrations. They conclude by outlining the solved and unsolved problems, with cost and debugging remaining significant hurdles.
Morgan Stanley's AlphaLab is an open-sourced multi-agent system designed to automate quantitative research. Initially, AlphaLab 1.0 automated code generation, backtesting, and experimentation. Facing challenges, AlphaLab 2.0 evolves to prioritize building robust, verifiable environments, which serve as reinforcement learning signals, enabling the system to meta-optimize itself. This shift redefines the human role from performing research to designing these critical environments.