Agents' next frontier: agent-to-agent and network effects — Jean-Denis Greze, Town
The talk reframes multi-agent systems not as 'agent-to-agent' interactions, but fundamentally as a search problem: ensuring that an LLM's context window contains the optimal information for a tool call. The primary technical barrier to achieving this ideal state—a single agent with access to all world information—is not context length, but privacy and security. Greze outlines five strategies (Shared Trust Boundaries, Custom Tools, Shared Silos, Human Conduit, Black Box) that attempt to approximate the optimal outcome while managing data leakage risks.
Key takeaways
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Reframing Agents as Search Problems
2:00
Most LLM systems are best viewed as search problems. The goal is engineering the system so that the context window contains the precise information needed before a tool call, maximizing the LLM's ability to return the best result.
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The Privacy Constraint (Coase Theorem)
5:24
The ideal state is one agent with access to all world information. However, privacy acts as a transaction cost, preventing this perfect data aggregation, which the Coase theorem highlights.
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Shared Silos and Sweeper Agents
13:59
A promising approach is creating shared silos (e.g., wikis) where a 'sweeper AI' automatically identifies and moves information from private spaces into public, company-wide knowledge bases.
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The Black Box Approach
This advanced method allows an LLM (in a 'black box' agent) to query multiple silos without pinging every human. It only asks the data owners for approval at the final moment of information sharing.
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The Future: Auto Mode
The frontier is 'auto mode,' where LLMs are trusted to automatically determine if a disclosure is low-sensitivity and can be shared without explicit human approval, scaling with model capacity.
