In the rapidly evolving landscape of AI-powered applications, Laurent Giraid stands out as a key figure bridging the gap between the raw potential of large language models and the structured discipline of software engineering. His work focuses on a critical, often-overlooked challenge: what happens when AI agents, designed to automate complex tasks, inevitably make mistakes? Giraid’s insights into building resilient, self-correcting AI systems offer a new paradigm for developers, moving beyond manual, painstaking error handling to more intelligent, automated frameworks. This conversation explores how to architect AI agents that can not only follow a workflow but also strategically search for the best path forward, learning from their missteps. We’ll touch on the practical benefits of separating an agent’s core logic from its problem-solving strategy, delve into a case study that yielded dramatic improvements in both coding efficiency and task accuracy, and discuss the future of these sophisticated, search-driven systems.
When an AI agent tasked with translating a large codebase makes a mistake, manually coding for backtracking can be as complex as the original program. How does EnCompass automate this process, and what specific steps must a programmer take to enable this automatic search and recovery?
It’s a familiar pain point for anyone building with these models. You create a brilliant, multi-step agent, maybe with thousands of lines of code defining its workflow, and it works beautifully… until it doesn’t. When an LLM makes a mistake, the thought of manually coding all the logic to backtrack, retry, and manage state can be absolutely soul-crushing; it feels like you’re building a second, even more complex program just for error handling. EnCompass completely changes this dynamic by taking on that burden. Instead of rewriting their logic, a programmer simply annotates their code at critical junctures. These are what we call “branchpoints”—locations where an LLM call happens or where any decision could lead down multiple paths. By adding these simple markers and noting what information might be useful for a search strategy, the developer gives the framework permission to take over. EnCompass then uses these annotations to automatically manage the state, clone the program runtime if needed, and explore different execution paths in parallel, all without the developer having to write a single line of backtracking code.
You’ve emphasized the separation between an AI agent’s workflow and its search strategy. Could you walk us through what this separation looks like in practice and why it is so beneficial for developers who want to experiment with different approaches like beam search or Monte Carlo tree search?
This separation is the philosophical core of the framework and, I believe, the key to unlocking true agility in AI development. In practice, it means you have two distinct pieces of code that live apart. One is your agent’s workflow—the Python program that says, “First, analyze this file, then call the LLM to translate it, then run a test.” This is the “what” and it remains clean and focused on the task. The second piece is the search strategy, which is the “how.” This is where you define how the agent should behave when it hits one of those branchpoints. Do you want it to explore the top three most likely LLM outputs, which is a beam search? Or do you want it to explore promising paths more deeply while still sampling other options, like a Monte Carlo tree search? Because these are decoupled, a developer can simply plug-and-play different search strategies without ever touching the agent’s core workflow. This is a game-changer. It transforms the process from a rigid, hard-coded implementation into a fluid, experimental science, allowing developers to rapidly iterate and find the optimal search method for their specific problem.
For an agent translating a code repository from Java to Python, implementing search reportedly reduced coding effort by over 80% while boosting accuracy by up to 40%. Could you explain what a “two-level beam search” is in this context and how it led to such a significant performance improvement?
Those numbers really bring the impact home, don’t they? The results were staggering. The agent was tasked with a large-scale translation, a process notoriously prone to subtle errors. The “two-level beam search” was the strategy we found worked best. Think of it as a search within a search. At the first level, the agent is translating the codebase one file at a time. Instead of just accepting the first translation, a beam search keeps the top few best-performing translations for each file. Then, at the second level, it looks at the combinations of these translated files across the entire repository. This allows it to find the set of file translations that are not just individually correct but also work best together as a cohesive whole. This holistic approach is what led to that incredible 15 to 40 percent accuracy boost. And the most beautiful part? We could experiment and land on this sophisticated strategy without a massive engineering effort. Implementing it with EnCompass required just a fraction of the code—348 fewer lines, to be exact—than it would have taken to build it from scratch.
The framework uses annotations called “branchpoints” to create multiple execution paths, much like a choose-your-own-adventure story. Could you provide a concrete example of a branchpoint in an agent’s workflow and explain how EnCompass uses it to manage parallel attempts and find an optimal solution?
The choose-your-own-adventure analogy is perfect. Imagine your agent’s code has a line that calls an LLM to refactor a specific function. This is a classic moment of uncertainty; the LLM could return several plausible, yet different, versions of the code. In a traditional program, you’d just take the first one and hope for the best. With EnCompass, you’d annotate that specific LLM call as a branchpoint. When the program executes and hits that annotation, the framework doesn’t just proceed with one answer. Instead, it effectively pauses and says, “This is a branching path.” Based on the chosen search strategy, like a beam search, it might ask the LLM for the top three most likely refactorings. EnCompass then creates three parallel “clones” of the program’s runtime state, each one continuing the workflow with a different version of that refactored function. It follows each of these “storylines” to see which one leads to the best outcome—perhaps measured by which version passes a suite of unit tests—and ultimately selects that optimal path.
EnCompass is designed for agents where a program defines a high-level workflow, but is less applicable to agents entirely controlled by an LLM. Can you elaborate on this distinction and discuss the unique challenges in applying structured search to an LLM that invents its own steps?
This is a critical distinction. EnCompass excels when there is a programmatic skeleton—a workflow defined in code that dictates the high-level steps. The agent might use an LLM for the tricky parts, like writing a piece of code or summarizing a document, but the overall sequence of operations is directed by the program. This structure provides the “rails” on which our search can run; the branchpoints are clearly defined within that structure. The other class of agents is quite different. These are agents where the LLM itself is the entire workflow. You give it a high-level goal, and it decides, on the fly, what to do next: “First, I’ll search the web. Then, I’ll write some code. Now, I’ll test it.” There’s no underlying program to annotate. Applying a structured search framework like EnCompass here is challenging because the “program” is ephemeral and exists only within the LLM’s own reasoning process. You can still apply search at the inference level, prompting the model to consider alternatives, but you can’t hook into a stable, programmatic workflow to manage state and parallel execution in the same way. It’s a fundamentally different and more dynamic challenge.
What is your forecast for search-driven AI agents?
I believe we are at the very beginning of a major shift. For the past couple of years, the focus has been on the raw capability of single-shot LLM prompts. The future, however, belongs to agents that treat LLM outputs not as final answers, but as possibilities to be explored. Search-driven architecture will become a standard design pattern for building reliable and high-performance AI systems. We will see this move beyond software engineering into complex domains like scientific discovery, where an agent could search through potential experiments, or hardware design, where it could explore vast blueprints for rockets or microchips. The core idea—separating a task’s logic from the strategy used to solve it—is incredibly powerful. It will allow us to build far more ambitious and robust agents that can tackle large-scale, multi-step problems with a level of resilience that feels less like brittle automation and more like genuine, persistent problem-solving.
