Autonomous Evaluation Architectures: Multi-Agent LLM Pipelines, Browser-Grounded Testing: Programmatic Alignment via DSPy, and Adversarial Robustness in Production Orchestration Systems

Abstract

Evaluating multi-agent large language model systems requires fundamentally different approaches than evaluating single-model outputs. Conventional benchmarks assess isolated model capabilities in controlled conditions, but production multi-agent pipelines exhibit emergent failure modes that only manifest through agent interactions across pipeline stages. An individual agent may produce valid output that, when consumed by a downstream agent, leads to semantically incorrect or structurally broken final artifacts, a class of failures that per-agent evaluation cannot detect by design. This article introduces AgentForge-Eval, a closed-loop evaluation architecture that combines browser-grounded execution testing, multi-layer deterministic and semantic assertion frameworks, and programmatic prompt alignment to autonomously detect, diagnose, and remediate multi-agent failures. Unlike static benchmarks that assess what models produce, AgentForge-Eval tests what multi-agent outputs actually do by executing generated artifacts in headless browser environments and feeding runtime results back into an iterative fix loop with formal convergence guarantees. Deployment in a production multi-agent pipeline demonstrates substantial improvements in first-pass acceptance rates, significant reductions in iterations required before approval, and detection of a materially larger share of failures than semantic judge evaluation captures alone. Programmatic optimization using the full evaluation stack as its objective achieves additional composite metric gains through automated cross-stage prompt alignment. The framework contributes a formal taxonomy of multi-agent failure modes and empirical evidence that browser-grounded evaluation captures a failure class that proxy-metric assessment cannot reach.