Most enterprises are experimenting with autonomous AI agents. Very few are deploying them safely at scale. According to McKinsey's "The State of AI in 2025" survey (November 2025), in no business function have more than ten percent of companies scaled AI agents into production. The failure is rarely a lack of ambition; it is a lack of visibility.

Unlike traditional software, autonomous agents generate their own logic on the fly. They bypass conventional security monitors, invoke tools and access data in ways that are difficult to audit after the fact, and operate across complex multi-agent workflows where a single misconfigured permission or policy gap can cascade into a significant security incident. What enterprises need is a new category of control infrastructure: one that operates at the moment a decision is being made, not after the damage is done.

LangGuard acts as a runtime enforcement layer for agentic workflows, monitoring and enforcing policy across the end-to-end chain of actions, decisions, tools, credentials, and intent that spans every system an agent touches. Databricks provides unified governance through Unity Catalog and AI Gateway—the system of record for data, models, and access policies. As enterprises deploy agents into production, the workflow itself also needs a runtime enforcement layer that extends those platform-level controls into every step of agent execution. That is where LangGuard fits in. LangGuard's governance engine, the GRAIL™ (Governance AI Run-time Links) data fabric, captures every agent action as multidimensional trace data and constructs a live knowledge graph of workflow behavior and context. When an agent attempts to invoke a tool, access a dataset, or call a model, LangGuard evaluates that action against policy before it executes, across every system the workflow touches, regardless of where it runs.

The scale of a production enterprise agentic deployment makes this genuinely hard. A single workflow may involve tens of coordinated agents, hundreds of tool invocations, multiple foundation models, and policies managed across fifteen or more enterprise Systems of Record, including IT ticketing systems like ServiceNow, IAM and IDP platforms, CRM systems like Salesforce, HR platforms like Workday, cloud security platforms like Wiz and CrowdStrike, contact center platforms like TalkDesk, MCP Gateways, and API Gateways. Governing this in real time, without impacting agent performance, demands infrastructure purpose-built for the problem.

The LangGuard team spent years building IBM QRadar, a multiple-time Gartner Magic Quadrant leader and one of the world's most widely deployed enterprise SIEM platforms. QRadar ingests and correlates petabytes of security telemetry per day under strict latency and reliability requirements. That experience taught us a hard lesson: database architecture is destiny. When we designed LangGuard's workflow governance engine, we faced the same challenge we had solved before: operational security data that arrives in unpredictable, high-intensity bursts, where every millisecond of decision latency matters and idle infrastructure spend is unacceptable. Traditional databases that couple compute and storage force you to provision for peak load and pay for that capacity around the clock. Lakebase's serverless model, which fully decouples compute from storage and scales to zero between bursts, was the answer we had always needed but didn't have access to when we were building QRadar. It matched the problem exactly.

Lakebase is a new category of operational database architecture that disaggregates compute from storage, allowing compute to scale elastically with workload demand while durable state lives independently in a replicated storage layer. Built on the open foundation of PostgreSQL, the lakebase architecture preserves everything developers rely on in a proven relational database while eliminating the infrastructure constraints that make traditional, monolithic RDBMS the wrong choice for the speed and scale that modern apps, agents, and AI demand.