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\section{Conclusion}
\label{sec:conclusion}
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This paper has documented the methodology and innovations of VQuant, a production-grade platform
that turns a natural-language request into a complete, reproducible quantitative financial
report. The system's organizing principle is a strict division of labor: the large language
model plans and synthesizes, while deterministic code retrieves data and performs computation.
This separation is what allows VQuant to combine the accessibility of a conversational interface
with the rigor demanded by financial analysis.

We highlighted four methodological contributions. The \emph{agentic orchestration loop}
(Section~\ref{subsec:loop}) exposes a curated catalogue of more than two hundred tools to the
model through a constrained, batched, fully streaming protocol, with a system prompt that routes
requests by complexity. The \emph{context-management algorithm} (Section~\ref{subsec:context})
keeps arbitrarily long conversations within the model's window by adaptively summarizing older
history while preserving recent turns verbatim. The \emph{single-call computation paradigm}
(Section~\ref{subsec:onecall}) consolidates data acquisition, estimation, and visualization into
one sandboxed Python execution, materially reducing latency relative to sequential tool calling.
And the \emph{unified document pipeline} (Section~\ref{subsec:docs}) emits PDF, Word, Beamer
slides, and live-formula spreadsheets from a single Markdown artifact. We described each of these
at the mechanism level---the eight-way event dispatch, the recursive loop and its
tool-use/tool-result invariant, the batching throttle, the two-stage context machinery, the
token-accounting correction, and the inter-process sandbox that exec's model-authored code and
captures its figures---together with the streaming experience, multi-model selection, cost
discipline, and security measures that make the system practical. The classical quantitative
methods the platform automates are conventional and recorded compactly in the appendix; the
contribution is the architecture, not the formulae.

The natural next step is empirical. A rigorous evaluation would benchmark VQuant against
baseline agentic patterns along three axes: \emph{quality}, via expert assessment of report
accuracy and completeness on a fixed task suite; \emph{efficiency}, via controlled measurement of
latency, token consumption, and dollar cost, isolating the contribution of the single-call
paradigm and the context-management algorithm; and \emph{reliability}, via systematic probing
for numerical and interpretive errors. Methodologically, the design admits several extensions:
incorporating provider-agnostic tool exposure to broaden the model layer; adding a verification
pass in which a second model audits the first model's narrative against the computed numbers; and
generalizing the deterministic-computation substrate beyond finance to any domain where
auditable numbers matter. We offer VQuant as a concrete, reproducible instance of \emph{agentic
financial intelligence}---a design pattern in which the language model is a disciplined
orchestrator of trustworthy tools rather than an unverified oracle---and as a foundation for the
evaluation agenda above.