Don't Vibe Code, Do Skele-Code: Interactive No-Code Notebooks for Subject Matter Experts to Build Lower-Cost Agentic Workflows

2026-03-18

AI Summary

This work addresses the challenge non-technical users face in efficiently constructing low-cost, maintainable AI agent workflows. To this end, we propose Skele-Code—a no-code, interactive notebook tailored for domain experts that integrates natural language and graph-based representations to support the progressive, modular construction of workflows through a code-first, agent-assisted paradigm. The key innovation lies in leveraging AI agents solely for code generation and error recovery, rather than task orchestration, while employing context engineering to substantially reduce token consumption. Skele-Code further enables workflow reuse, sharing, and embedding, establishing a scalable, cost-effective, and maintainable development paradigm for AI-driven workflows.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Skele-Code is a natural-language and graph-based interface for building workflows with AI agents, designed especially for less or non-technical users. It supports incremental, interactive notebook-style development, and each step is converted to code with a required set of functions and behavior to enable incremental building of workflows. Agents are invoked only for code generation and error recovery, not orchestration or task execution. This agent-supported, but code-first approach to workflows, along with the context-engineering used in Skele-Code, can help reduce token costs compared to the multi-agent system approach to executing workflows. Skele-Code produces modular, easily extensible, and shareable workflows. The generated workflows can also be used as skills by agents, or as steps in other workflows.

Problem

• no-code
• agentic workflows
• AI agents
• token cost
• subject matter experts

Innovation

• Skele-Code
• no-code
• agent-supported workflow
• context-engineering
• token cost reduction