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

TL;DR

TopoPilot is a reliable, extensible framework that automates complex scientific visualization workflows with verification mechanisms, significantly improving success rates over baseline systems in topological data analysis tasks.

Contribution

It introduces a two-agent architecture with verification and guardrails to enhance reliability in autonomous scientific visualization workflows, especially for topological data analysis.

Findings

Achieves over 99% success rate in simulated multi-turn conversations.

Reduces code-generation errors through separation of interpretation and verification.

Demonstrates robustness against adversarial and infeasible requests.

Abstract

Recent agentic systems demonstrate that large language models can generate scientific visualizations from natural language. However, reliability remains a major limitation: systems may execute invalid operations, introduce subtle but consequential errors, or fail to request missing information when inputs are underspecified. These issues are amplified in real-world workflows, which often exceed the complexity of standard benchmarks. Ensuring reliability in autonomous visualization pipelines therefore remains an open challenge. We present TopoPilot, a reliable and extensible agentic framework for automating complex scientific visualization workflows. TopoPilot incorporates systematic guardrails and verification mechanisms to ensure reliable operation. While we focus on topological data analysis and visualization as a primary use case, the framework is designed to generalize across visualization domains. TopoPilot adopts a reliability-centered two-agent architecture. An orchestrator agent translates user prompts into workflows composed of atomic backend actions, while a verifier agent evaluates these workflows prior to execution, enforcing structural validity and semantic consistency. This separation of interpretation and verification reduces code-generation errors and enforces correctness guarantees. A modular architecture further improves robustness by isolating components and enabling seamless integration of new descriptors and domain-specific workflows without modifying the core system. To systematically address reliability, we introduce a taxonomy of failure modes and implement targeted safeguards for each class. In evaluations simulating 1,000 multi-turn conversations across 100 prompts, including adversarial and infeasible requests, TopoPilot achieves a success rate exceeding 99%, compared to under 50% for baselines without comprehensive guardrails and checks.