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Theory

Harness Execution Rollout and Kernel Dynamics

Executing an agent harness h=(M,P,A,F)h = (M, P, A, F) with a frozen backbone model πψ\pi_\psi on a task τ\tau with capability registry CτC_\tau under protocol Π\Pi induces a closed-loop trajectory:

ξ∼Rollout(τ,πψ,h,Cτ;Π)=(s1,e1,o1,…,sT,eT,oT)\xi \sim \text{Rollout}(\tau, \pi_\psi, h, C_\tau; \Pi) = (s_1, e_1, o_1, \dots, s_T, e_T, o_T)

Execution initializes with an empty event history xi_{<1} = emptyset and protocol state s1∈Ss_1 \in \mathcal{S}. At each step tt, the action module emits et∈A=U⊔Ye_t \in \mathcal{A} = \mathcal{U} \sqcup \mathcal{Y}, where U\mathcal{U} is the set of executable calls and Y\mathcal{Y} is the set of terminal outputs. The execution kernel then evaluates ete_t:

ot={Exec(et;Ct),et∈U⊥,et∈Yo_t = \begin{cases} \text{Exec}(e_t; C_t), & e_t \in \mathcal{U} \bot, & e_t \in \mathcal{Y} \end{cases}

where ⊥\bot is the terminal null observation. The history updates via concatenation xi_{le t} = xi_{<t} oplus (s_t, e_t, o_t), terminating when et∈Ye_t \in \mathcal{Y} or upon reaching a protocol- or budget-bounded stopping limit TT.

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Updated 2026-09-25

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Prep Sessions

Adaptive Agent Runtimes: Architecture, Synthesis, and State Control @ University of Michigan - Ann Arbor

Ch.1 Agent Harness Principles and Architecture - Adaptive Agent Runtimes: Architecture, Synthesis, and State Control @ University of Michigan - Ann Arbor

Four-Module Modular Harness Protocol - Adaptive Agent Runtimes: Architecture, Synthesis, and State Control @ University of Michigan - Ann Arbor