Trust Anchors for Agentic Scientific Computing: Analytical Navier–Stokes Solutions as Verification Primitives in Multi-Agent AI Ecosystems

The emergence of agentic AI systems—autonomous agents that discover, communicate, and collaborate through protocols such as MCP and A2A—is transforming scientific computing. In computational fluid dynamics, specialized AI agents (PINN solvers, neural operator surrogates, data assimilation modules) can now autonomously generate, exchange, and consume numerical solutions to the Navier–Stokes equations. However, this autonomy creates a critical trust problem: when agents produce conflicting predictions with no human in the loop, how is correctness established? We argue that exact and semi-analytical solutions of the Navier–Stokes equations serve as trust anchors—computationally verifiable ground truths that enable agents to self-validate, diagnose failure modes, and establish mutual trust without human intervention. We propose a Verified Multi-Agent Solver (VMAS) architecture with a structured interaction protocol, a dynamic trust ledger, and a diagnostic protocol mapping six documented failure modes of physics-informed machine learning to selective analytical solutions. As a proof of concept, we train three architecturally distinct PINN solvers (vanilla, Fourier-feature, and causal) on four trust anchors and show that the observed error patterns exhibit the predicted failure mode signatures.