Build the full operational awareness layer — ecosystem observability, failure memory, evidence coverage, and operational debt management.
Target Outcome
A functioning operational intelligence layer: entity graph, failure confidence scoring, GEO coverage, and a live observability system.
Prerequisites
The complete build log for this platform — tech stack decisions, MDX pipeline, failure archive, GEO system. The architecture documentation for everything described in the remaining steps.
Entity hierarchy, relationship types, knowledge inheritance patterns, and the query API. Understand the design before implementing your own graph.
Recurring failure memory, prevention inheritance, debugging lineage, and confidence scoring. The design rationale for lib/failure-memory.ts.
Create lib/operational-memory.ts following the entity ID convention [type]:[slug]. Register your failures, lessons, case studies, and docs as entities. Add typed relationships.
Create lib/failure-memory.ts. Define RAW_FAILURES with instance counts, prevention step flags, and playbook flags. The confidence scoring rubric is defined in the failure-memory-architecture doc.
Create lib/geo-intelligence.ts with PLATFORM_ENTITIES and GEO_QUERY_TAXONOMY. Implement computeEntityDensity() and scoreAnswerability() following the scoring rubric in the geo-intelligence-architecture doc.
Evidence quality standards for the operational archive. Apply to all future failure reports and case studies.
See This Pathway in Practice
A real case study demonstrates this pathway executed in full production context.
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