VReAct: Verification as an Architectural Layer for LLM Agents: A V-Model Design, and a Pilot Study of Its Deterministic Core
Large language model (LLM) agents built on the ReAct pattern concentrate four responsibilities in one model: selecting a strategy, choosing each action, formatting it, and judging whether the result is adequate. Nothing outside the generative loop can reject its output, so an agent that cannot progress does not report failure; it runs until an external budget terminates it. We propose VReAct, which treats verification as an architectural layer for ReAct agents by adapting the software-engineering V-model: specification levels descend from requirements to individual steps, each paired with a dedicated verifier; a deterministic controller enforces every verdict; and only verification outcomes write memory. A rejection thus localizes the level that introduced the fault, and an agent halts by declining rather than by exhaustion. Each verifier separates a zero-cost deterministic gate from an optional LLM judge, so the contribution and cost of each can be measured independently. A pilot study implements the acceptance- and unit-level verifier pairs, comparing five configurations that share one executor, tool set, and scorer and differ only in verification components, on the four-hop stratum of MuSiQue with an 8B-parameter backbone. Across 47 executions, the two unverified configurations answered none of ten questions, every run ending at a step cap or provider token limit; the verified configuration without a planner answered eight of ten and abstained on the rest. Deterministic gates produced eight of nine observed corrections at zero marginal cost, and planning degraded performance once verification was present. These results characterize termination behavior, not accuracy at scale; we outline a twelve-month plan to complete and evaluate the full architecture, including the integration-level pair the pilot omits.