2 posts tagged with "schema-design"

Most teams designing LLM agents spend considerable effort on tool selection and system prompt wording. Almost none of them think carefully about what their tools return. That's a mistake with compounding consequences — because the shape of a tool response determines how well the agent can reason about it, how much context window it consumes, and how often it hallucinates an interpretation the tool never intended.

Tool output schema design is infrastructure, not plumbing. Get it wrong and your agent will fail in ways that look like reasoning problems when they're actually schema problems.

Most teams building agents treat their database schema as a backend concern. The schema was designed by engineers, for engineers, following decades of relational database best practices: normalize aggressively, avoid redundancy, split reference tables, enforce foreign keys. This approach is correct for OLTP systems. It is often wrong for AI agents.

When an agent reads your schema to figure out how to answer a question, it is not parsing a data structure. It is constructing a mental model of your business. If your schema was built for application code that already understands the domain, the agent will be working against a map drawn for someone else. The result is hallucinated joins, incorrect aggregations, and tool call chains that should take two steps but take eight.