ControlA: Agentic Workflow Control Mechanisms for Reliable Science

AI-driven scientific discovery has emerged as a transformative fifth paradigm in research, with agentic AI playing an increasingly prominent role across scientific domains. Agentic AI can enable collaborative AI-human or even fully autonomous decision-making, but it also introduces significant reliability challenges due to the dynamic and evolutionary nature of the AI agents. Specifically, foundation model-powered agents are prone to generating hallucinated, misleading, or adversarial outputs that can propagate silently through workflows and corrupt downstream results. In this paper we present a conceptual framework for a unified approach that integrates agentic workflow-level instrumentation and agent-level safeguards to enhance the reliability of the wider system, particularly critical in science. Embedding these mechanisms into a provenance-augmented infrastructure enables early detection, containment, and recovery from erroneous behavior, ultimately enhancing reliability and reproducibility in AI-assisted scientific workflows.