Multi-agent debate systems incorporate retrieval-based verification to ground agent reasoning with evidence. However, this approach assumes the availability of data and the informative intent of the source text. These conditions are frequently unmet in venture capital, particularly in early-stage startup evaluation. In this industry, evidence is predominantly private or unavailable, and pitch decks use persuasive rhetoric that prior research shows can shift agent conclusions. To measure how agents navigate these conditions, we evaluate how claim verification alters multi-agent debate using a stratified sample of deals from a real venture fund. We find that verification changes the agent’s final investment decision but not its reasoning. Furthermore, agents are insensitive to which claim a verification label is attached to. Reassigning labels to the wrong claims lowers the decision to the same extent. This indicates that the primary bottleneck in automated screening is fundamental claim verifiability rather than retrieval quality.

Venture capital (VC) investors face a large number of investment opportunities but only invest in few of these, with even fewer ending up successful. Early-stage screening of opportunities is often limited by investor bandwidth, demanding tradeoffs between evaluation diligence and number of opportunities assessed. To ease this tradeoff, we introduce DIALECTIC, an LLM-based multi-agent system for startup evaluation. DIALECTIC first gathers factual knowledge about a startup and organizes these facts into a hierarchical question tree. It then synthesizes the facts into natural-language arguments for and against an investment and iteratively critiques and refines these arguments through a simulated debate, which surfaces only the most convincing arguments. Our system also produces numeric decision scores that allow investors to rank and thus efficiently prioritize opportunities. We evaluate DIALECTIC through backtesting on real investment opportunities aggregated from five VC funds, showing that DIALECTIC matches the precision of human VCs in predicting startup success.