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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.03571 |
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| _version_ | 1866914178644574208 |
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| author | Li, Zhening Solar-Lezama, Armando Yue, Yisong Zheng, Stephan |
| author_facet | Li, Zhening Solar-Lezama, Armando Yue, Yisong Zheng, Stephan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03571 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EnCompass: Enhancing Agent Programming with Search Over Program Execution Paths Li, Zhening Solar-Lezama, Armando Yue, Yisong Zheng, Stephan Artificial Intelligence Machine Learning Programming Languages 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. |
| title | EnCompass: Enhancing Agent Programming with Search Over Program Execution Paths |
| topic | Artificial Intelligence Machine Learning Programming Languages |
| url | https://arxiv.org/abs/2512.03571 |