🤖 Building AI systems? Context - and Flink - is all you need!

Traditional batch architectures cannot meet the needs of modern AI systems, which increasingly operate as autonomous agents requiring millisecond-latency access to both data and its metadata context. Batch ETL introduces unavoidable staleness, relies on fragile orchestration for backfills, and pushes governance and lineage downstream into the analytical estate — too late for AI systems that must make real-time operational decisions. This creates accuracy issues, model drift, and regulatory blind spots.

This talk explains why organizations need to adapt streaming-native Real-time Context Engines built on continuous data processing, incremental enrichment, and first-class data governance. Using engines like Apache Flink, enriched event streams are governed, and lineage-tracked as part of the streaming pipeline itself, shifting left toward the point of data generation. We detail how event-time semantics, schema evolution, and incremental state updates enable deterministic behavior and full reproducibility without manual pipeline rewrites.

A core capability is materialized context: every enriched and governed dataset is projected into a strongly consistent, queryable in-memory table, continuously updated from the event log. Both the data and its metadata context are available to AI agents through open interfaces like MCP (Model Context Protocol) or REST API endpoints. This enables agents not only to retrieve the freshest state, but also to inspect the provenance, quality constraints, and governance rules associated with that state which is critical for reliability, trust, and regulatory compliance.

Equally important is the role of memory management, indexing, and schema intelligence in serving the optimal context to stateless AI models. Because AI systems have no internal memory, every prompt requires reconstructing the most relevant slice of context: per case, customer, conversation, transaction, or semantic topic. This demand necessitates granular in-memory indexing, adaptive caching, and strong ontology awareness to locate and deliver only the minimal but most meaningful context at low latency. Organizations must therefore deeply understand their data ontologies, entity relationships, and schema evolution patterns to design memory-efficient, fine-grained indexes that ensure AI agents always operate with precise, fully updated context rather than broad, stale datasets.

We will show architectural blueprints and operational patterns for building scalable, low-latency, governance-first context layers suitable for high-stakes AI-driven operations. In that context, we will highlight the regulatory implications: when AI systems make recommendations or act automatically, organizations must document the context and lineage of the data that influenced the decision. Real-time lineage tracking ensures auditability, verifiable traceability, and accountability.