Search papers, labs, and topics across Lattice.
The paper introduces BONSAI, a mixed-initiative workspace that enables human-AI co-development of visual analytics applications by combining custom development with AI code generation. BONSAI employs a modular four-layer architecture and a structured four-phase development process to ensure distributed agency and full provenance of both human and AI contributions. Case studies demonstrate BONSAI's ability to efficiently create novel tools and reconstruct complex VA applications from research paper descriptions.
Forget fragile monoliths and unauditable AI chaos: BONSAI offers a structured workspace where humans and AI agents collaboratively build visual analytics applications with speed and rigor.
Developing Visual Analytics (VA) applications requires integrating complex machine learning models with expressive interactive interfaces. Developers face a stark trade-off: building tightly-coupled monoliths plagued by fragile interdependencies, or relying on restrictive, simplistic frameworks. Meanwhile, unconstrained, single-shot AI code generation promises speed but yields unstructured, unauditable chaos. The core challenge is combining the control and expressiveness of custom development with the efficiency of AI generation under strict constraints. To address this, we introduce BONSAI, a mixed-initiative workspace for the multi-agent co-development of VA applications. BONSAI utilizes a modular four-layer architecture (hardware, services, orchestration, application) that allows human and AI developers to independently contribute reusable components. The workspace incorporates this architecture into a structured four-phase development process (plan, design, monitor, and review), ensuring distributed agency and full provenance, where all human and AI contributions are structurally bounded and tracked. We evaluate BONSAI through case studies demonstrating the efficient creation of novel tools and the rapid reconstruction of complex VA applications directly from research paper descriptions. Ultimately, this paper contributes a conceptual workflow, a scalable architecture, and an integrated system that successfully balances AI's generative speed with the structural rigor required for complex VA development.
