Autonomous AI agents are a black box for most enterprises, creating a significant governance gap. Unlike traditional software, agents dynamically generate their own logic, bypassing standard security monitors and making auditing difficult. This invisible problem is why fewer than 10% of companies have successfully scaled AI agents into production, according to McKinsey.
LangGuard aims to solve this by providing a runtime enforcement layer for agentic workflows. It monitors and enforces policies across every action, decision, tool, and credential an agent uses, extending platform-level controls from tools like Databricks' Unity Catalog and AI Gateway.
Runtime Enforcement Meets Platform Governance
The core of LangGuard's solution is its GRAIL™ data fabric, which captures agent actions as multidimensional trace data to build a live knowledge graph. This graph allows LangGuard to evaluate policy decisions in real time before an agent executes an action, such as invoking a tool or accessing data.
Governing complex, multi-agent workflows that span dozens of systems of record is exceptionally challenging. A single misstep can cascade into a major security incident.
Databricks Lakebase: The Foundation for Real-Time Control
Databricks Lakebase, the first fully managed, serverless Postgres database built on the lakehouse, underpins LangGuard's capabilities. Its architecture disaggregates compute from storage, enabling elastic scaling and scale-to-zero compute. This is crucial for handling the bursty nature of agentic workloads without over-provisioning infrastructure.