The Complete Gradientflo Learning Workflow
Gradientflo orchestrates 8 phases with 7 engines to transform code into capability, from intake to validation.
How 8 Agents Work Together

Gradientflo's Core Agent System
The individual deeply understands the codebase's evolution, indexing repositories and commits while documenting changes. They create dependency maps and track ownership to ensure project clarity and organization.
This system detects when learning is necessary, identifies new concepts, recognizes risky changes, and observes signs of confusion.
This system creates a dynamic knowledge graph that connects various concepts to their corresponding code. It effectively tracks relationships between these concepts and updates in real-time, ensuring that the information remains current and relevant.
The applied learning generator creates micro-courses that utilize real repository examples to enhance the learning experience. It also validates the user's understanding of the material.
This tool allows you to create reusable assets, produce detailed diagrams, summarize architectural designs, and easily link to relevant code.
This tool provides a conversational approach to explanations, addressing questions and answers effectively. It clarifies pull requests and their intent while also alleviating the workload for senior team members.
This tool outlines your learning journey by recommending subsequent steps, emphasizing essential concepts, and identifying potential risk areas.
This feature assesses the impact of learning, monitors progress, evaluates practical application, and identifies areas where knowledge is lacking.
