Pylar – structured data access layer for AI agents (www.pylar.ai)

Pylar is a structured data access layer that lets organizations give AI agents controlled, auditable access to database information without exposing raw DB credentials. Instead of letting agents run arbitrary queries, teams define safe, scoped SQL views with fine‑grained permissions and parameterized inputs; Pylar automatically turns those views into MCP tools that can be published to any agent builder via a single link. That removes the need for ETL or data migration—model a view once, publish tools across teams and environments, and keep agents constrained to pre-approved data surfaces. Technically, Pylar integrates with BigQuery, Snowflake, Postgres, Redshift and more, and includes SOC 2–ready controls, an explicit audit trail for every call, and visibility into who queried what. It also provides built-in monitoring and evals to track agent performance, query success rates, and data usage so you can measure safety and efficacy in production. For the AI/ML community this reduces operational and compliance risk, speeds safe deployment of data-driven agents, and creates a reproducible governance layer that scales agent use across organizations while preserving privacy and security.