Don't Offer What Can't Be Done: Deterministic Executability Gating for LLM Skill Selection at Scale
Researchers introduce a three-stage pipeline to prevent LLM agents from attempting impossible tasks by adding a deterministic executability check.
- Semantic relevance alone is insufficient for reliable LLM agent tool selection.
- A three-stage pipeline (recall, deterministic gate, and execution) improves agent reliability.
- Deterministic gating prevents agents from attempting tasks that violate account-state constraints.
- The method was successfully deployed in Wix's Helpmate customer care assistant.
As LLM-based agents move into production, they often struggle with 'kill selection' when faced with large libraries of possible actions. A common failure occurs when an agent selects a tool that is semantically relevant to a user request but technically impossible to execute due to the user's current account status or system constraints.
To solve this, the researchers behind Wix's Helpmate assistant implemented a three-stage pipeline. The process begins with a semantic matcher to narrow down relevant skills, followed by a deterministic gate that filters out actions that cannot be performed under current conditions. This prevents the model from wasting tokens or hallucinating execution steps for invalid requests.
By separating semantic relevance from technical feasibility, the system ensures that agents only attempt tasks that are actually actionable. This approach reduces error rates in customer service environments where account-specific permissions and states dictate what an AI can or cannot do.
Provides a practical architecture for building reliable agentic workflows in production.
Reduces error rates and computational waste in customer-facing AI assistants.
Offers a concrete example of combining probabilistic LLM logic with deterministic software engineering.
- Deterministic Gating
- A hard-coded logic check used to filter out invalid options before an AI attempts to process them.
- Semantic Matcher
- A component that uses vector embeddings to find items that are conceptually related to a query.
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