TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visualization

Abstract

Recent agentic systems demonstrate that large language models can generate complex codebases from natural language prompts. Despite this progress, they present reliability concerns for scientific visualization due to invalid operations, subtle errors, and failure to resolve underspecified inputs. These challenges are compounded in real-world workflows that exceed the complexity of standard benchmarks. In this work, we introduce TopoPilot, a reliable and extensible agentic framework for automating complex scientific visualization workflows. We focus on topological data analysis and visualization as a primary use case, while designing the framework to generalize to broader visualization domains. TopoPilot employs a two-agent architecture: an orchestrator translates user prompts into workflows composed of atomic backend actions, while a verifier evaluates workflows prior to execution. Its modular design isolates components and supports new topological descriptors and domain-specific workflows without modifying the core system. It also employs numerous deterministic guardrails to ensure semantic consistency and accurate execution. To systematically improve reliability, we develop a taxonomy of failure modes and employ targeted safeguards for each class of error. Across 1,000 simulated multi-turn conversations spanning 100 prompts, including adversarial and infeasible requests, TopoPilot achieves a success rate exceeding 95%, compared with under 50% for a baseline without comprehensive guardrails and verification. We also show that TopoPilot outperforms Claude Code and Codex on similar prompts in terms of accuracy, completion time, and token usage.