The transition from generative AI prototypes to production-grade agents introduces a specific engineering hurdle: reliability. LLMs are stochastic by nature, and a prompt that works once may fail on the second attempt. To mitigate this, development teams often wrap core business logic in complex error-handling loops, retries, and branching paths.

The entanglement problem in agent design

Current approaches to agent programming often conflate two distinct design aspects. The first is the core workflow logic, or the sequence of steps required to complete a business task. The second is the inference-time strategy, which dictates how the system navigates uncertainty, such as generating multiple drafts or verifying outputs against a rubric.

When these are combined, the resulting codebase becomes brittle. Implementing a strategy like "best-of-N" sampling requires wrapping the entire agent function in a loop. Moving to a more complex strategy, such as tree search or refinement, typically requires a complete structural rewrite of the agent's code.

Decoupling logic from search to boost AI agent scalability

The ENCOMPASS framework addresses this by allowing programmers to mark "locations of unreliability" within their code using a primitive called branchpoint().

These markers indicate where an LLM call occurs and where execution might diverge. The developer writes the code as if the operation will succeed. At runtime, the framework interprets these branch points to construct a search tree of possible execution paths.

This architecture enables what the authors term "program-in-control" agents. Unlike "LLM-in-control" systems, where the model decides the entire sequence of operations, program-in-control agents operate within a workflow defined by code. The LLM is invoked only to perform specific subtasks. This structure is generally preferred in enterprise environments for its higher predictability and auditability compared to fully autonomous agents.

By treating inference strategies as a search over execution paths, the framework allows developers to apply different algorithms – such as depth-first search, beam search, or Monte Carlo tree search – without altering the underlying business logic.

Impact on legacy migration and code translation

The utility of this approach is evident in complex workflows such as legacy code migration. The researchers applied the framework to a Java-to-Python translation agent. The workflow involved translating a repository file-by-file, generating inputs, and validating the output through execution.

In a standard Python implementation, adding search logic to this workflow required defining a state machine. This process obscured the business logic and made the code difficult to read or lint. Implementing beam search required the programmer to break the workflow into individual steps and explicitly manage state across a dictionary of variables.

Using the proposed framework to boost AI agent scalability, the team implemented the same search strategies by inserting branchpoint() statements before LLM calls. The core logic remained linear and readable. The study found that applying beam search at both the file and method level outperformed simpler sampling strategies.

Cost efficiency and performance scaling

Controlling the cost of inference is a primary concern for data officers managing P&L for AI projects. The research demonstrates that sophisticated search algorithms can yield better results at a lower cost compared to simply increasing the number of feedback loops.

Adopting this architecture requires a change in how development teams view agent construction. The framework is designed to work in conjunction with existing libraries such as LangChain, rather than replacing them. It sits at a different layer of the stack, managing control flow rather than prompt engineering or tool interfaces.