Frontier LLM agents perform complex interactive tasks, but their inference costs motivate transferring their experience to smaller open-source agents.
Distillation and latent communication typically require target-model training or access to source-model internals.
We study how textual frontier-model trajectories can provide reusable local controls for a frozen agent.
Inspired by protocol reverse engineering, we introduce $\textit{Agentic Latents}$, comprising anchored states and dynamic profiles of feedback--response cycles elicited by replay in the target model.
Our training-free framework, $\textbf{REVEAL}$ ($\textbf{REV}$erse $\textbf{E}$ngineering $\textbf{A}$gentic $\textbf{L}$atents), uses these profiles and trajectory outcomes to match and select local events from successful and failed rollouts of the same task and source model.
Their response-transition contrasts yield a target-specific steering direction, applied before each response.
Within-benchmark evaluations on Terminal-Bench 2.1 and Humanity's Last Exam with tools report higher accuracy than unmodified agents across three targets.
Representation analyses and behavioral case studies support reusing frontier-model experience as local activation control.