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Bottom line: Markets for autonomous agents face three pressures together: a finite context window, verification that can take longer than a decision, and defaults that spread faster than a person can react. The paper argues that mechanisms built for human markets struggle to meet all three, and proposes the Agent Coupling Protocol (ACP).

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Full text (03_The_Orchestration_Trilemma.pdf) and versions on Zenodo.

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Key points

  • The trilemma is supported by a semi-formal argument, not a theorem.
  • ACP combines an exponential context barrier, an escrowed stake that limits how far defaults travel, and a winner-determination rule that weighs price against context load and stake.
  • The paper states a cascade bound that follows from branching-process theory, and an equilibrium characterisation as a conjecture.
  • In an extended Generative Agents simulation (1,000 agents, 30 simulated days, 100 seeds), ACP’s systemic failure rate was 0.011 against 0.089–0.156 for four baselines: 8.1 to 14.2 times lower, and 9.1 times lower than the median baseline. Its cognitive-collapse rate was 0.003 against 0.028–0.045.

Limitations

The simulation uses a cached language-model stand-in at a decision frequency below the regime in which the trilemma is stated. The results have not been replicated with deployed agents.

Abstract

Markets for autonomous language-model agents run into three pressures at once. An agent’s context window is finite, verifying a counterparty can take longer than the agent’s decision interval, and a single default can spread through a chain of obligations faster than a person can react. We call this combination the Orchestration Trilemma and argue, with a semi-formal argument rather than a theorem, that mechanisms built for human markets struggle to meet all three constraints together. We then describe the Agent Coupling Protocol (ACP), a mechanism that combines an exponential context barrier, an escrowed stake that limits how far defaults travel, and a winner-determination rule that weighs price against context load and stake. We state a cascade bound that follows from branching-process theory, and we state an equilibrium characterisation as a conjecture. In an extended Generative-Agents simulation with 1,000 agents over 30 simulated days and 100 seeds, ACP’s systemic failure rate was 0.011 against 0.089-0.156 for four baselines, a reduction of 8.1 to 14.2 times (9.1 times against the median baseline), and its cognitive-collapse rate was 0.003 against 0.028-0.045. These are simulation results with a cached language-model stand-in, at a decision frequency below the regime in which the trilemma is stated, and have not been replicated with deployed agents.
  • Agent Coupling Protocol: ACP is a ten-stage protocol that weighs provider reliability more heavily as an orchestrator’s context fills. In simulation, honest providers earn 4.8 to 7.6 times more than malicious ones.
  • Liquidity Coupling: Escrowing a fraction of each contract stops insolvency cascades when AI agents hire agents. In a 10,000-agent simulation, mean cascade depth fell by 70.2%.
  • Marginal Utility of Context: An exponential barrier price on context use makes a rational agent compress memory early. In a 50-agent simulation it raised task success from 0.42 to 0.73.

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Last modified on November 15, 2026