## Overview The 2026 approach to building data agents replaces the traditional semantic layer (upfront schema mapping, curated definitions, maintained metadata) with adaptive context management. Instead of teaching the agent the database schema in advance, the agent reads raw data models directly and learns from user corrections over time. This shift matters because semantic layers are expensive to build, expensive to maintain, and become stale as schemas evolve. Adaptive context treats every user correction as a permanent learning event — the agent builds its own understanding through interaction rather than requiring pre-configured knowledge. With models like Opus 4.6 and hybrid search, the agent can navigate unfamiliar data structures autonomously. The practical impact: non-technical teams access data through natural interfaces (Slack, Notion) without requiring a data team to build and maintain intermediary layers. Each correction improves future queries, creating a compounding accuracy curve. ## Cross-Domain Applications - **Agent Architecture**: The adaptive context pattern generalizes beyond data agents. Any agent operating on structured systems can learn from corrections rather than requiring exhaustive upfront configuration. This is the same compounding improvement pattern seen in agent error harvesting. - **Knowledge Management**: Parallels the progressive summarization approach — start with raw material, layer understanding through interaction, build toward distilled knowledge over time rather than requiring perfect organization upfront. ## References - Jamie Quint, March 2026