Agentic Generation and Evolution of Knowledge Models

2026-04-13 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

TrustModel is a proposed agent-based platform designed for the continuous generation and evolution of "living" knowledge models (KMs) in complex software systems like autonomous vehicles and robotics. It addresses the challenge of KMs becoming incomplete, inconsistent, or outdated as systems evolve. The platform comprises three agentic subsystems: Modeling, for constructing and updating KMs; Conformance, for assessing KM alignment with the system and its environment; and Evolution, for generating guidance to synchronize KMs with changes. TrustModel can be instantiated for model-based testing (MBT) and supports other Model-Driven Engineering (MDE) activities such as requirements and assumption monitoring, architectural drift tracking, and change impact assessment, positioning living KMs as a foundation for dependable engineering.

Key takeaway

For software architects and MDE practitioners managing complex, evolving systems, TrustModel offers a framework to ensure your knowledge models remain accurate and trustworthy. By adopting an agentic approach, you can automate the continuous synchronization of models with system changes and environmental dynamics. This reduces manual effort and improves the reliability of model-driven activities like testing and requirements monitoring, ultimately enhancing system dependability. Consider how its three agentic subsystems could integrate with your existing MDE toolchain.

Key insights

TrustModel uses LLM-based agents to continuously generate, validate, and evolve knowledge models for dependable software engineering.

Principles

• Knowledge models must be "living" to remain useful.
• Agentic subsystems enable continuous KM synchronization.
• LLMs are key enablers for agentic capabilities in MDE.

Method

TrustModel's agentic subsystems (Modeling, Conformance, Evolution) collaboratively construct, validate, and update KMs. Modeling generates/updates KMs, Conformance assesses alignment, and Evolution provides guidance based on feedback.

In practice

• Instantiate TrustModel for model-based testing (MBT).
• Apply to requirements and assumption monitoring.
• Use for architectural drift tracking.

Topics

• Agentic AI
• Knowledge Models
• Model-Driven Engineering
• Model-Based Testing
• Software Evolution
• LLM-based Agents

Best for: Research Scientist, AI Scientist, Software Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.