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

arXiv: https://arxiv.org/abs/2605.19186 · HTML: https://mlsanity.com/paper/2605.19186/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 70 references that are in the map.
- First posted on arXiv 18 May 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 10 of 10)

- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions](https://mlsanity.com/paper/2510.25445/) (2025)
- [Agent-OM: Leveraging LLM Agents for Ontology Matching](https://mlsanity.com/paper/2312.00326/) (2023)
- [Large Language Models Miss the Multi-Agent Mark](https://mlsanity.com/paper/2505.21298/) (2025)
- [Governance of Autonomous Agents on the Web: Challenges and Opportunities](https://mlsanity.com/paper/2202.02574/) (2022)
- [Signifiers as a First-class Abstraction in Hypermedia Multi-Agent Systems](https://mlsanity.com/paper/2302.06970/) (2023)
- [Fetch.ai: An Architecture for Modern Multi-Agent Systems](https://mlsanity.com/paper/2510.18699/) (2025)
- [The AnIML Ontology: Enabling Semantic Interoperability for Large-Scale Experimental Data in Interconnected Scientific Labs](https://mlsanity.com/paper/2604.01728/) (2026)
- [Knowledge Affordances for Hybrid Human-AI Information Seeking](https://mlsanity.com/paper/2604.27539/) (2026)
- [Aber-OWL: a framework for ontology-based data access in biology](https://mlsanity.com/paper/1407.6812/) (2014)

## Built on by (most cited first, 1 of 1)

- [Knowledge Graph Re-engineering Along the Ontological Continuum (extended version)](https://mlsanity.com/paper/2605.22093/) (2026)

## Abstract (arXiv)

Two decades ago, the Semantic Web Services community was asked how agents with different ontological commitments could discover, compose, and invoke web services coherently. The response was OWL-S and WSMO: formally grounded capability descriptions specifying what a service could do, what the agent must already know for invocation to be epistemically sound, and how ontological mismatches could be formally bridged. Current KG metadata standards such as VoID and DCAT describe what a KG contains, yet say nothing about what a specific agent can prove from it, what closure assumptions govern empty results, or whether the agent's task vocabulary is grounded in the schema. Furthermore, in deployed KGs the governing schema DL and the operative entailment regime can diverge: an epistemic failure mode invisible to current metadata. We revisit and extend these insights for the KG setting with a four-dimensional formal framework; Semantic Expressivity, Agentic Discoverability, Task-Relative Grounding, and Epistemic Trust Scope, from which we derive the Agentic Affordance Profile (AAP): a semantic layer above VoID and DCAT enabling principled KG selection, composition, and failure diagnosis at agent planning time. The four dimensions operationalise the affordance structure of the Ontological Continuum at the individual-agent level, specifically for kg selection, composition, and failure diagnosis. A worked example drawn from a scholarly-search task concretely grounds the framework, and identifies the formal, computational, and engineering work needed to realise AAP-based affordance matching at scale though a five-point research agenda.