Python Fragment

Python Fragment is an executable, AI-ingestible code block embedded as part of a structured memory object, enabling agentic systems to perform dynamic reasoning, calculations, and conditional logic directly from published content. It pairs procedural code with provenance, trust metadata, and semantic relationships, making it retrievable, interpretable, and runnable by AI agents.

Unlike static Procedure Fragments that describe steps in natural language, a Python Fragment contains actual Python code that can be executed deterministically, allowing AI systems to produce answers or perform tasks with precision and repeatability.

🧠 Full Definition

A Python Fragment typically includes:

• Code body — the Python source implementing the intended logic
• Inputs and parameters — declared variables and expected argument types
• Outputs — defined return values or structured output formats
• Execution context — environmental or dependency requirements
• Provenance metadata — source, author, version, and publication date
• Trust Layer — authority level, execution confidence, and applicable scope
• Semantic relationships — links to related glossary terms, procedures, or data definitions

This structure enables AI systems to retrieve a Python Fragment, verify its trustworthiness, and execute it to generate context-aware results.

📌 Key Characteristics of Python Fragment

• Contains runnable Python code embedded in a memory object
• Supports deterministic execution for repeatable outputs
• Links directly to procedural, eligibility, or explainer fragments for integrated workflows
• Includes trust and provenance metadata for safe execution

💡 Why It Matters

Python Fragments bridge the gap between static knowledge and executable intelligence, allowing AI systems to not only recall and explain information but also act on it in real time. This capability is critical for agentic workflows that require calculations, eligibility checks, data transformations, or decision-making logic.

🌐 WebMEM Perspective

Within WebMEM, Python Fragments represent the Procedural Execution tier of the Visibility Stack. They transform structured retrieval into executable reasoning, enabling AI to shift from passive answer generation to active task completion.

🗣️ Example Use

“We embedded a Python Fragment in our insurance glossary so AI agents can calculate deductible thresholds based on user inputs.”

🔗 Related Terms

• Procedure Fragment
• Eligibility Fragment
• Explainer Fragment
• Functional Memory
• Trust Layer