Enterprise AI agents face a key engineering challenge as they move from prototype to production: the inherent unpredictability of large language models (LLMs) makes it hard to write reliable workflows. A prompt that works once might fail the next time, so developers often end up intertwining core business logic with complex error-handling, branching, retries, and fallback code — quickly making systems harder to maintain and scale.
To tackle this, researchers from Asari AI, MIT CSAIL, and Caltech propose a new architectural model that decouples the agent’s logic (what it should do) from its search or execution strategies (how to recover when LLM calls vary). Their framework, called Probabilistic Angelic Nondeterminism (PAN), and its Python implementation ENCOMPASS, let developers write the ideal, straightforward path of an agent’s workflow (the “happy path”) while deferring handling of alternative outcomes to a separate runtime engine.
In practice, ENCOMPASS introduces a simple mechanism — branchpoint() — that marks where an LLM’s output might vary. At runtime, those branch points form a search tree of possible execution paths, allowing the system to explore multiple branches (e.g., via beam search or backtracking) without polluting the main workflow with error-handling logic. This separation reduces technical debt and supports experimenting with different strategies independently of the core code.
By cleanly isolating search strategies from program logic, this approach helps AI agents scale more effectively in real-world environments where LLM outputs are seldom deterministic. It enables developers to write clearer workflows, reuse search mechanisms across projects, and potentially improve performance and maintainability as systems grow in complexity.
