PARCER as an Operational Contract to Reduce Variance, Cost, and Risk in LLM Systems

TL;DR

PARCER is a framework that enhances governance, consistency, and control in LLM systems by transforming interactions into structured, versioned artifacts with strict operational phases and observability.

Contribution

It introduces PARCER, a novel operational contract framework that formalizes and governs LLM interactions to reduce variance, cost, and risk.

Findings

Implements a structured seven-phase governance process

Introduces decision hygiene and fallback mechanisms

Provides systemic observability with OpenTelemetry

Abstract

Systems based on Large Language Models (LLMs) have become formidable tools for automating research and software production. However, their governance remains a challenge when technical requirements demand absolute consistency, auditability, and predictable control over cost and latency. Recent literature highlights two phenomena that aggravate this scenario: the stochastic variance inherent in the model's judgment (often treated as "systemic noise") and the substantial degradation of context utilization in long inputs, with critical losses when decisive information is diluted in the middle of the prompt. This article proposes PARCER as an engineering response to these limitations. The framework acts as a declarative "operational contract" in YAML, transforming unstructured interactions into versioned and executable artifacts. PARCER imposes strict governance structured into seven operational phases, introducing decision hygiene practices inspired by legal judgments to mitigate noise, adaptive token budgeting, formalized recovery routes (fallbacks) for context preservation, and systemic observability via OpenTelemetry. The objective of this work is to present the conceptual and technical architecture of PARCER, positioning it as a necessary transition from simple "prompt engineering" to "context engineering with governable governance".