The Price of Over-Delegation: Stackelberg Liability Design for Agentic AI Handoffs

Tomoya Hoshino

Keywords: Stackelberg Games, Multi-Agent AI Workflows, Liability Design, Potential Games

TL;DR: We model agentic AI handoffs as a Stackelberg liability-design game and show that partial liability internalization causes excessive workflow fragmentation, while optimal regulation can recover near-first-best welfare.

Abstract:

As LLM-based agents are increasingly deployed
in sequential delegation chains,
each handoff can obscure accountability
for the final output,
leading to context loss, audit overhead,
and diffusion of responsibility.
We formulate this governance problem
as a Stackelberg game:
a regulator sets a liability share,
and developers choose a workflow partition
via a boundary-insertion game
on a sequential workflow DAG.
The induced game is an exact potential game
for every liability share $\gamma \in (0,1]$,
and under a continuous relaxation
admits a unique interior equilibrium.
We prove an over-fragmentation theorem:
when developers only partially internalize
handoff externalities ($\gamma < 1$),
the equilibrium delegation depth
strictly exceeds the social optimum,
and the resulting welfare loss
admits a scale-free closed-form expression
independent of workflow size, agent productivity,
and handoff-cost scale.
We characterize the optimal liability share $\gamma^*$
via a first-order condition
that balances the marginal welfare gain
against the marginal enforcement cost,
and derive comparative statics.
Under optimal regulation,
residual welfare loss scales quadratically
with enforcement cost,
suggesting that reductions in enforcement costs
yield more-than-proportional welfare gains.

Email Sharing: We authorize the sharing of all author emails with Program Chairs.

Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

Paper Type: Standard paper

Submission Number: 19