Arca, an AI-native financial services firm, runs its client business through long-horizon agents that own goals end to end across days of stop-start work. Aman Singh, who leads AI there, explains the signal system, operational context, harness, persistent memory, and shared spaces that let agents pull in people at the right moment, and why steering agents becomes the way to steer the business.
Jerry, CEO of LlamaIndex, argues that as AI agents move from answering questions to running long-lived workflows, documents become their most important context source. He walks through the evolution from simple RAG to agent harnesses, explains why PDFs are hard to parse, and presents the company's approach to OCR, extraction with grounding, and document search. He closes by outlining unsolved problems: agent-native document formats, in-place editing, versioning, distilling frontier workflows into cheaper models, and long-horizon background agents.
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.