Asenion AI Management System: Taxonomy & Feature Explanation

1. AI Projects

  • An AI project is the simpliest way to start tracking your AI initiatives.

Each AI project can be subjected to:

  • Qualitative Assessments
  • Quantitative Tests

Sometimes, that is enough.But for more complex or high risk systems that require more detailed compliance assessments and monitoring, an AI project can capture additional information under AI Use Cases,

2. AI Use Cases

  • An AI Use Case is a discrete job-to-be-done in the AI project.
  • Implemented using:
    • AI Models
    • AI Agents
    • Datasets

Each AI Use Case can also be subjected to:

  • Qualitative Assessments
  • Quantitative Tests

3. AI Models

  • Types:
    • Predictive Models (e.g., XGBoost, Logistic Regression)
    • LLMs (e.g., GPT-4, Gemini-2.0)
  • Role: Power specific functions directly.

Each AI model can also be subjected to:

  • Qualitative Assessments
  • Quantitative Tests

4. AI Agents

  • Each Agent performs one task, possibly subdivided into subtasks.
  • Agents orchestrate tasks such as:
    • Web scraping
    • API calls
    • Other Agents
    • RAG pipelines etc.
  • Typically one agent in a mult-agent system is mapped to a single LLM.

Each AI Agent can also be subjected to:

  • Qualitative Assessments
  • Quantitative Tests

5. Datasets

  • Each Dataset is use to track and evaluate dataset used by the AI use case.

Each dataset can also be subjected to:

  • Qualitative Assessments
  • Quantitative Tests

6. Evaluation Framework

Policy Packs = Qualitative Assessments

Assurance Packs = Quantitative Tests