Building Data Pipelines for AI Agents: Ingestion, Transformation, and Storage

Data pipelines for AI agents differ from traditional ETL in one critical way: the output is not a clean table — it is a knowledge store the agent can query in real time. The ingestion layer connects to source systems: CRM APIs, helpdesk exports, documentation wikis, database snapshots. Fivetran and Airbyte handle the mechanical ingestion; custom extractors handle proprietary systems. The transformation layer cleans, deduplicates, and chunks documents into agent-readable segments. Chunking strategy matters enormously: too small loses context, too large floods the context window. Recursive character splitting at 500-800 tokens with 20% overlap is a solid default. The storage layer indexes chunks for retrieval. Vector databases (Pinecone, Weaviate, pgvector) enable semantic search. BM25 keyword indexes handle exact-match queries. Hybrid search combining both consistently outperforms either alone. The pipeline must run continuously, not just once — stale knowledge bases degrade agent accuracy over time. Implement incremental sync so only changed documents re-embed.