Discoverable Agent Knowledge -- A Formal Framework for Agentic KG Affordances (Extended Version)

2026-05-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The Agentic Affordance Profile (AAP) framework is introduced to address the challenge of autonomous agents assessing Knowledge Graph (KG) suitability for specific tasks, a gap in current metadata standards like VoID and DCAT. Extending insights from Semantic Web Services (OWL-S, WSMO), the AAP framework defines KG affordance across four dimensions: Semantic Expressivity (ℰ), Agentic Discoverability (𝒟), Task-Relative Grounding (𝒢), and Epistemic Trust Scope (ℛ). This formal framework enables principled KG selection, composition, and failure diagnosis during agent planning. A five-point research agenda outlines the necessary formal, computational, and engineering work for scaling AAP-based affordance matching. A practical example demonstrates how three KGs yield distinct planner verdicts for a scholarly-search task, illustrating how specific dimensional shortfalls map to targeted remedial actions.

Key takeaway

For AI Architects designing LLM-orchestrated agent systems, you should integrate the Agentic Affordance Profile (AAP) framework into your KG discovery and planning phases. This allows your agents to formally assess Knowledge Graph suitability for specific tasks, preventing unsound conclusions from mismatched data. By leveraging AAP, you can enable principled KG selection, composition, and precise failure diagnosis, ensuring robust and epistemically sound agent interactions with knowledge resources.

Key insights

The Agentic Affordance Profile (AAP) framework enables agents to formally assess Knowledge Graph fitness for tasks using four dimensions.

Principles

• KG affordance is task-relative, not intrinsic.
• Metadata must declare epistemic commitments.
• Definability-based coverage is key for grounding.

Method

The AAP framework characterizes KGs across four dimensions (ℰ, 𝒟, 𝒢, ℛ) to compute a feasibility predicate for agent tasks, enabling principled selection, composition, and failure diagnosis.

In practice

• Publish AAP profiles for KGs in registries.
• Use AAP scores as preconditions for agent tool-use.
• Diagnose task failures by dimensional shortfalls.

Topics

• Agentic AI
• Knowledge Graphs
• Semantic Web Services
• OWL-S/WSMO
• Agentic Affordance Profile
• Ontology Alignment

Best for: Research Scientist, AI Scientist, AI Architect, AI Engineer

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