Observability for AI Agent Database Query Pipelines
Agents fail silently in ways traditional monitoring can't catch.
Callum Dzenga
Section
6 stories in MCP & Databases.
Agents fail silently in ways traditional monitoring can't catch.
Agents burn through database budgets in seconds, requiring rethinking rate limits entirely.
Agents need semantic and tool-result caching to match their bursty, low-repetition query patterns.
Agents overwhelm schemas built for humans; programmatic calling fixes the structural limits.
Design your MCP server to keep schema in Resources, not Tools.
CHKit brings schema-as-code discipline to analytics databases running autonomous agent pipelines.