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Agentic Development Stack

2 posts · Jan 2026 – Sep 2026

What this trail covers

C3 AI is operationalizing its Agentic Development Stack, a comprehensive platform that fuses advanced coding agents with enterprise-grade infrastructure. This stack enables the rapid and reliable development of secure, durable, and fully deployable enterprise AI applications. Key components include high-value application templates, a domain-expert LLM trained for enterprise software and the C3 AI ecosystem, and the C3 AI Model Context Protocol (MCP) Server for seamless integration of external co-pilots and tools. This post details the practical application of this stack in building an asset management application, showcasing iterative development, data source integration, backend API generation, automated testing, access control management, and enabling data science workflows.

All C3 AI trails
  1. Data Center Sourcing App Built in Days with C3 CodeThis post details the construction of a data center sourcing application in 72 hours using C3 Code. It showcases the platform's capability to generate a full-stack application, including: a connected ontology-based data model for parts, suppliers, and prices; integration of live commodity and economic data from FRED; interactive 3D visualizations linking hardware to sourcing and cost data; a news pipeline for supply chain risk monitoring; and a mixed-integer linear programming optimizer that considers cost, capacity, and risk. It also highlights the development of a conversational agent for natural language interaction with the application's features, and emphasizes the production-ready nature of applications built with C3 Code.
  2. From Prompt to Production: Building Enterprise-Grade Applications with C3 AI’s Agentic Development StackThis post details the practical application of the C3 AI Agentic Development Stack for building enterprise-grade applications. It showcases how the stack, comprising application templates, a domain-expert LLM, and the MCP Server, enables developers to go from prompt to production rapidly. The post provides a step-by-step walkthrough of building an asset management application, demonstrating iterative UI refinement, connecting to production data sources (Snowflake), generating complex backend APIs, automated unit test generation and execution, defining custom access roles, and enabling JupyterLab for data science. It highlights the integration with IDEs like VSCode and tools like GitHub Copilot, emphasizing the fusion of coding agents with enterprise infrastructure for speed and rigor.