Enterprise Data Stewardship
Agentic data quality, lineage, schema drift, and semantic drift management with steward review and replayable decisions.
Purpose
Keep enterprise data usable for agents, analytics, and operations as schemas, business meanings, and ownership change. ContextOS turns data stewardship into an agentic workflow: detect drift, plan remediation, validate lineage, propose fixes, and route high-impact changes through accountable stewards.
Why this is agentic-first
Data quality work is continuous and contextual. A pipeline break can be caused by schema drift, stale ownership, a changed business definition, missing lineage, or an upstream vendor change. IBM describes agentic data management as intent-driven orchestration where agents generate a plan, execute and validate workflows, adapt to schema or semantic changes, and keep guardrails active.
For ContextOS, this use case is foundational: agents cannot make trustworthy decisions if the knowledge substrate is stale, semantically inconsistent, or unaudited.
Context Pack
| Layer | Required entries |
|---|---|
decision_layer.decision_specs[] | data.drift.classify, data.quality.remediate, data.semantic_change.approve, data.lineage.certify |
policy_layer.policy_bundles[] | Data classification, PII handling, domain ownership, lineage, retention, metric-definition policy |
policy_layer.approval_gates[] | GATE_DATA_STEWARD, GATE_PRIVACY_REVIEW, GATE_METRIC_OWNER |
tooling_layer.adapter_registry[] | adp_catalog.query, adp_lineage.trace, adp_quality.profile, adp_pipeline.patch, adp_semantic_layer.update |
memory_layer.write_classes_allowed | schema_pattern, semantic_correction, data_quality_outcome |
evaluation_layer.eval_targets[] | drift detection precision, data-quality SLA, steward override rate, lineage completeness |
Agent roles
| Agent | Responsibility | Boundary |
|---|---|---|
| Drift Agent | Detects schema, freshness, distribution, and semantic drift. | Read-only until remediation is approved. |
| Lineage Agent | Maps upstream and downstream impact. | Cannot certify lineage alone. |
| Quality Agent | Profiles data and proposes remediation. | Can write only to sandbox datasets. |
| Semantic Agent | Compares metric and entity definitions across systems. | Cannot change canonical definitions without owner gate. |
| Steward Agent | Checks owner, privacy, lineage, and rollback requirements. | Can block or escalate. |
Execution flow
invokeAgentarrives withintent=data.steward, dataset ID, anomaly type, and business domain.- Compiler pins catalog metadata, lineage graph, ontology version, privacy class, and owner map.
- Drift Agent classifies the anomaly as schema, freshness, distribution, lineage, or semantic drift.
- Lineage Agent builds impact radius across dashboards, features, workflows, and agent tools.
- Quality Agent proposes remediation and tests it in a sandbox.
- Semantic Agent compares business definitions and flags meaning changes not visible in schema.
- Steward Agent verifies ownership, privacy policy, and rollback.
- Tool Gateway patches pipeline or semantic layer only after the required owner gate.
- ContextOS emits a
DecisionRecordwithdecision_key="data.quality.remediate".
Decision gates
| Gate | Trigger | Required evidence |
|---|---|---|
GATE_DATA_STEWARD | Any pipeline, catalog, or lineage change affecting certified data. | owner, lineage impact, quality profile, rollback |
GATE_PRIVACY_REVIEW | PII class, retention, access, or masking change. | privacy class, affected users, legal basis |
GATE_METRIC_OWNER | Semantic-layer or KPI-definition change. | old/new definition, downstream impact, approval owner |
Failure modes
- Silent semantic drift - schema stays stable but business meaning changes; Semantic Agent must compare definitions and usage.
- Pipeline patch without impact analysis - Tool Gateway blocks writes unless lineage impact is attached.
- Privacy class downgrade - Privacy review gate must approve any weaker handling rule.
- Bad remediation loop - sandbox validation must pass before production patch.
- Memory poisoning - proposed schema or semantic learnings enter review, not automatic promotion.
Metrics
- Drift detection precision and recall.
- Mean time to classify and remediate data incidents.
- Certified lineage coverage.
- Steward approval latency.
- Privacy gate violations blocked.
- Downstream incident recurrence after remediation.
Research signals
- IBM’s agentic data management article frames ADM as dynamic validation across data movement, lineage checks, semantic validation, and guardrails.
- IBM’s agentic workflows overview defines agentic workflows as multistep, adaptive, tool-using processes.
- Infosys and HFS report cites data readiness and governance as major constraints to scaling agentic AI.