PI Tag Mapping Using Contextualization Agents
BKOAI converted PI tag mapping into a structured reasoning workflow and stored the decision path in a context graph, creating reusable decision memory with improved accuracy and traceability
Goals
- Improve PI tag-to-equipment mapping accuracy and consistency.
- Turn mapping from a one-time lookup into a reusable contextualization workflow.
- Preserve decision logic for future reuse and governance.
Challenges
- PI tags are often inconsistent, abbreviated, or incomplete.
- A single tag may match multiple equipment candidates.
- Traditional methods preserve the final result but lose the rationale, alternatives, constraints, and validation history.
Solutions
- Use Contextualization Agents to expand tag metadata and compare equipment candidates.
- Apply process context, equipment hierarchy, and operating constraints.
- Embed self-reflection and human verification into the workflow.
- Store the complete decision history in a structured Context Graph.
Results
- Reuse prior mapping decisions instead of restarting inference.
- Reduce hallucination risk and manual rework.
- Improve consistency, traceability, and auditability.
- Scale contextualization across assets and deployments.
.svg){kind=small}
.svg){kind=small}
.svg){kind=small}
