EnCompass: Enhancing Agent Programming with Search Over Program Execution Paths

• URL: http://arxiv.org/abs/2512.03571v1
• Date: Wed, 03 Dec 2025 08:50:16 GMT
• Title: EnCompass: Enhancing Agent Programming with Search Over Program Execution Paths
• Authors: Zhening Li, Armando Solar-Lezama, Yisong Yue, Stephan Zheng,
• Abstract summary: Current approaches to agent programming often entangle two aspects of agent design: the core workflow logic and the inference-time strategy.<n>We introduce "probabilistic angelic nondeterminism" ("PAN"), a programming model that disentangles these two concerns.<n>We present three case studies that demonstrate how the framework lets the programmer quickly improve the reliability of an agent and easily switch between different inference-time strategies.
• Score: 30.69327461098545
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce a new approach to agent programming, the development of LLM-based agents. Current approaches to agent programming often entangle two aspects of agent design: the core workflow logic and the inference-time strategy (e.g., tree search). We introduce "probabilistic angelic nondeterminism" ("PAN"), a programming model that disentangles these two concerns, allowing the programmer to describe the agent workflow and independently experiment with different inference-time strategies by simply changing a few inputs. We provide an implementation of PAN in Python as the EnCompass framework, which uses a Python decorator to compile agent workflow programs into a search space. We present three case studies that demonstrate how the framework lets the programmer quickly improve the reliability of an agent and easily switch between different inference-time strategies, all with little additional coding.

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