Context Engineering: Purpose-Built Data Pipelines for AgentsDraft

The most common agent architecture failure isn't model quality — it's data quality. Organizations connect agents to raw data sources (Slack, email, tickets, calendars) and expect coherent reasoning. What they get is hallucination grounded in noise: outdated threads, duplicate information, context without meaning. The problem is that "just connect everything" conflates data access with data understanding.

Context engineering treats agent data as a first-class infrastructure concern. This paper introduces event sourcing with CQRS as the foundation for agent data pipelines — an architecture that separates immutable facts from purpose-built knowledge projections, provides complete auditability, and enables temporal queries that agents need for reasoning about change over time.

What You'll Learn

• Why raw data connections create silent degradation in agent reasoning, and how to detect it before it compounds
• How to treat incoming data as an event source: immutable events with structured provenance, feeding purpose-built read models with correlation identifiers
• Why multiple processors attached to the same event stream is the natural pattern — topic extractors, decision loggers, urgency detectors, all running independently on the same facts
• Provenance through the pipeline: structured derived artifacts, targeted regeneration, and traceable errors from agent output back to raw source data
• Why source-specialist knowledge builders (one per data source) outperform generic ingestion, and how their outputs compose into hierarchical knowledge flows
• The mandatory guardrails for composable knowledge: quality gates, full data lineage, and deterministic fallbacks

Who This Is For: Data engineers, platform architects, and ML infrastructure teams building the data layer for production agent systems.