Separating logic from inference improves the scalability of AI agents by decoupling the core workflow from the execution strategy.
Moving from a generative AI prototype to a production-grade agent poses certain engineering hurdles: reliability. LLM is probabilistic in nature. A prompt that works once may fail on the second attempt. To alleviate this, development teams often wrap core business logic in complex error handling loops, retries, and branching paths.
This approach creates maintenance issues. The code that defines what the agent should do is closely intermixed with the code that defines how to deal with model unpredictability. A new framework proposed by researchers at Asari AI, MIT CSAIL, and Caltech suggests that a different architectural standard is needed to scale agent workflows within the enterprise.
This study introduces a programming model called Probabilistic Angelic Nondeterminism (PAN) and a Python implementation called ENCOMPASS. This approach allows developers to create a “happy path” for the agent’s workflow while delegating inference-time strategies (such as beam search and backtracking) to another runtime engine. This separation of concerns provides a potential route to reducing technical debt while improving the performance of automated tasks.
Entanglement problems in agent design
Current approaches to agent programming often confuse two different design aspects. The first is the core workflow logic, or the sequence of steps required to complete a business task. The second is the reasoning time strategy. This determines how the system navigates uncertainty, such as generating multiple drafts or validating output against a rubric.
When you combine these, the resulting codebase is vulnerable. To implement strategies like “best-of-N” sampling, the entire agent functionality must be wrapped within a loop. Moving to more complex strategies, such as tree searches and refinements, typically requires a complete rewrite of the structure of the agent’s code.
Researchers argue that this tangle limits their experiments. If a development team wants to switch from simple sampling to a beam search strategy to improve accuracy, they often need to redesign the application’s control flow. This high cost of experimentation means teams often settle for suboptimal reliability strategies to avoid engineering overhead.
Improve AI agent scalability by separating logic from search
The ENCOMPASS framework addresses this problem by allowing programmers to mark “untrusted locations” in their code using a primitive called branchpoint().
These markers indicate where LLM calls occur and where execution can branch. Developers write code as if the operation would succeed. At runtime, the framework interprets these branch points to build a search tree of possible execution paths.
This architecture enables what the authors call “program-in-control” agents. Unlike “LLM-in-control” systems, where the model determines the entire sequence of operations, program-in-control agents operate within a workflow defined in code. LLMs are only called to perform specific subtasks. This structure is generally preferred in enterprise environments because it is more predictable and auditable compared to fully autonomous agents.
By treating inference strategies as execution path searches, the framework allows developers to apply various algorithms such as depth-first search, beam search, and Monte Carlo tree search without changing the underlying business logic.
Legacy migration and code conversion implications
The usefulness of this approach is evident in complex workflows such as legacy code migration. The researchers applied this framework to a Java to Python conversion agent. This workflow involved converting the repository file by file, generating input, and validating the output through execution.
In the standard Python implementation, adding search logic to this workflow required defining a state machine. This process made the business logic difficult to understand and the code difficult to read and lint. Implementing beam search required programmers to break the workflow into individual steps and explicitly manage state across a dictionary of variables.
Using the proposed framework to increase the scalability of AI agents, the team implemented the same search strategy by inserting a branchpoint() statement before the LLM call. The core logic remained linear and easy to read. In this study, we found that applying beam search at both the file and method level resulted in better performance than simpler sampling strategies.
The data shows that separating these concerns allows for better scaling laws. Performance improved linearly with the logarithm of inference cost. The most effective strategy found, fine-grained beam search, was also the most complex to implement using traditional coding methods.
Expanding cost efficiency and performance
Controlling inference costs is a top concern for data professionals managing the bottom line of AI projects. This study demonstrated that using a sophisticated search algorithm can yield better results at a lower cost than simply increasing the number of feedback loops.
In a case study involving the “Reflexion” agent pattern (LLM critiquing its own output), the researchers compared scaling the number of refinement loops and using a best-first search algorithm. The search-based approach achieved comparable performance to standard refinement methods, but at a lower cost per task.
This finding suggests that the choice of inference strategy is a factor in cost optimization. Externalizing this strategy allows teams to strike a balance between their compute budget and desired accuracy without having to rewrite their applications. Low-risk internal tools use cheap and greedy search strategies, while customer-facing applications use more expensive and exhaustive searches, all running on the same codebase.
Adopting this architecture requires development teams to change the way they view agent construction. This framework is designed to work with existing libraries such as LangChain, rather than replacing them. It sits on another layer of the stack and manages control flow rather than prompt engineering or tool interfaces.
However, this approach is not without engineering challenges. This framework reduces the code required to implement search, but does not automate the design of the agent itself. Engineers still need to identify the correct location of branching points and define verifiable success metrics.
The effectiveness of the search function depends on the system’s ability to score specific paths. In the code transformation example, the system can run unit tests to verify correctness. In more subjective areas such as summarization and creative generation, defining reliable scoring functions remains a bottleneck.
Additionally, this model relies on the ability to copy program state at branch points. Although the framework handles variable scoping and memory management, developers must ensure that external side effects such as database writes and API calls are managed correctly to prevent duplicate actions during the search process.
Impact on AI agent scalability
The changes represented by PAN and ENCOMPASS are consistent with the broader software engineering principle of modularity. As agent workflows become core to operations, maintaining them requires the same rigor applied to traditional software.
Hardcoding probabilistic logic into business applications creates technical debt. This makes testing, auditing, and upgrading the system difficult. By separating the inference strategy from the workflow logic, you can optimize both independently.
This separation also facilitates better governance. If a particular search strategy introduces hallucinations or errors, it can be adjusted globally without evaluating the codebase of individual agents. This simplifies versioning of AI behavior, a requirement in regulated industries where the “how” of decisions is as important as the outcome.
This study shows that as inference time computing scales, managing execution paths becomes more complex. Enterprise architectures that isolate this complexity are likely to prove more durable than those that allow penetration into the application layer.
See: Intuit, Uber, and State Farm trial AI agents in enterprise workflows
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