Long-Horizon Agents Need Experiments, Not Just Prompts — Erina Karati
This talk outlines the challenges of building multi-agent AI systems that maintain state and social consistency over long time horizons. The speaker introduces Project Paradox, a modular, stateful framework for game agents. The core problem—the degradation of social consistency (e.g., losing the source of a rumor)—is addressed by implementing an 'Auto Research' layer. This layer functions as an experimental loop, running controlled scenarios, collecting structured traces, and proposing constrained policy changes only if a balanced scorecard improves, thereby moving agent improvement from simple prompt tuning to systematic, verifiable engineering.
Key takeaways
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Long-Horizon Agents Need Experiments, Not Just Prompts
20:33
Improving multi-agent behavior requires running controlled scenarios and collecting structured traces, rather than relying solely on prompt engineering or single-run demos. The goal is to evaluate entire runs, not just single answers.
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Provenance and Source Attribution are Critical
Simple Retrieval-Augmented Generation (RAG) memory is insufficient. Agents must track where information came from (firsthand, secondhand, verified, uncertain) to maintain social consistency.
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Optimization Requires a Balanced Scorecard
Instead of optimizing for a single metric (e.g., 'agent quality'), a balanced scorecard must measure multiple dimensions like reach, source retention, uncertainty preservation, action consistency, and privacy containment.
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The Auto Research Loop
13:33
The proposed solution is a meta-system that defines a controlled scenario, runs the agents, scores the behavior, and proposes small, constrained changes to the agent protocol (the 'editable surface').