Ontology-constrained planning and validation

An ontology gives an agent a shared vocabulary for goals, entities, relations, and action arguments. It can help resolve that “Atlas” is a Project, that a Project has an owning Team, and that a ChangeRequest targets a Service. It can also support inferred classifications used during planning. Ontology grounding is valuable only when the ontology release, inference rules, and operational constraints are explicit.

Map user language to stable identifiers through an entity-linking step that can return alternatives or abstain. Labels and synonyms help retrieval but do not establish identity. The plan should carry the selected IRI and resolution evidence. If a write targets an ambiguous entity, stop for clarification. A human-readable plan can render labels while the executable form retains identifiers.

Use class and property definitions to validate action arguments. If restartService requires a Service and the selected entity is known only as an Application, the ontology may establish a subclass relation—or it may not. Do not let the model assume colloquial equivalence. Range and domain entailments can add types but are not closed-world input validation. SHACL or JSON Schema verifies that required explicit fields are present and well formed.

Represent action schemas in an ontology only where semantic reuse helps. A class of ProductionWriteAction can inherit risk controls. Properties can connect an action proposal to target, evidence, expected effect, and required approval. However, a policy engine remains responsible for live authorization. OWL inference cannot check a rapidly changing approval, budget, or separation-of-duties rule by itself.

Preconditions may be queried from the knowledge graph, but the result has a release and completeness boundary. No recorded active incident does not prove no incident exists. For a high-risk action, require an authoritative live tool or a graph freshness guarantee. Annotate preconditions as open-world informational, validated closed-world, or externally verified. This prevents absence from becoming permission.

SHACL can validate a proposed plan graph before execution. Shapes require action type, target node kind, evidence count, change window, and approval reference. SPARQL-based constraints can check controlled relations, but custom queries need bounded cost and clear authorization. Validate both the individual action and cross-step invariants such as “no production execution before staging verification.”

Ontology constraints can guide decomposition. If a requested goal concerns a RegulatedService, the agent can add required compliance evidence because the class definition or policy mapping says it is needed. This is a transparent rule, not a model intuition. Record which axiom or policy introduced the step. Avoid excessive inference that adds actions users did not authorize; a derived requirement can block or request approval, not silently broaden external effects.

Ontology version changes can invalidate stored plans. A new disjointness axiom may reveal bad identity; a new shape may require evidence; a deprecated property may change a query. Pin every trajectory to an ontology and shapes release. Before resuming a paused plan under a new release, rerun semantic validation and show material differences.

Reasoning explanations should be evidence objects. If a service is classified as Critical because it supports a regulated workflow, preserve the asserted relations, axiom identifiers, reasoner version, and release. The action proposal cites that derivation. If support is retracted, plans depending on the classification enter needs-review rather than continuing from stale state.

Evaluate entity-link accuracy, semantic type validity, intended and forbidden entailments, SHACL plan conformance, policy separation, ontology-change invalidation, and user comprehension of explanations. The purpose is to make agent decisions more constrained and inspectable—not merely to add a graph lookup before the same unconstrained loop.