The industry’s first proactive agent action enforcement engine provides definitive runtime privileges for agent workflows.
LangGuard today announced the general availability of LangGuard Arbiter© for decisively enforcing agent actions at the Databricks Data and AI Summit. Earlier this year, LangGuard launched GRAIL Data FabricTM to give enterprises detailed visibility into every action their agents take. LangGuard Arbiter© provides definitive policy control over all actions before the agent takes action.
Every company is just days away from having an agent incident they can’t explain to the board. LangGuard Arbiter© provides a behaviorally enforced, decisive voice that is automatically proven. ”
— Venkat Raghavan, co-founder, LangGuard Inc.
In April 2026, an agent deleted a customer database in less than 9 seconds after deducing everything from a credential mismatch to a destructive API call. The agent had permission to access the system. No humans were asked. At that particular moment, the authority for that particular action did not exist. Agents then documented all safety rules that were violated.
Two pressing challenges are simultaneously impacting enterprise AI adoption.
The first is the agent action surface. Agents access production systems through a growing world of tools, including MCP servers, REST APIs, CLI commands, SQL interfaces, and increasingly headless SaaS platforms. All systems that once required a human login and click or a predefined API connection can now be accessed directly by agents as they see fit. The action surface no longer has boundaries. We are expanding faster than any company can manage its inventory.
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The second is the agent harness. Companies deploy agents through Claude Code, Cursor, Codex, Cowork, Hermes, and in-house built harnesses. Enterprise SaaS platforms also activate built-in agents by default. All of these harnesses can reach the same extended action surface by querying the database, modifying customer records, executing transactions, committing code, and calling external APIs without requiring human authority.
There is a runtime permission gap between these two instructions. That is, there is no mechanism to enforce human oversight of what the agent actually does. Bridging this gap requires two capabilities that no company has had before. One is the action system, which monitors all agent behavior across all sessions and systems, and the other is an enforcement engine, which acts on what the system knows at the moment the agent decides to act.
LangGuard GRAIL Data FabricTM is an action system for agent workflows, purpose-built to record what agents do, not just what they access. Every action, every session, every system touched is displayed in a continuous, structured context graph. LangGuard Arbiter© enforces the moment an agent acts.
LangGuard Arbiter© operates at the agent’s action surface, the layer between the agent’s reasoning and the enterprise system on which it operates. Every action the agent attempts is evaluated by the Arbiter© before reaching the target system. The results are conclusive. Allow, block, or escalate to human decision authority. All generated policies are instantly red-teamed against hostile agent behavior before being accepted. Only passing policies are validated and a policy ledger is created that includes the provenance chain from compliance intent to enforcement. That record is a compliance artifact required by auditors, and businesses have not been able to generate it automatically until now.
With LangGuard Arbiter©, any enterprise team responsible for agent deployment has a ready-made layer of control built for them.
Data and AI teams: Generate and enforce runtime policies for enterprise, SOX, GDPR, and other regulatory needs without creating rules for every combination of agents and actions.
IT teams: Detect and contain agent over-agency and token maximization by flagging agents operating beyond their intended range and budget overruns before they reach production systems.
Audit Team: Segregation of Duties (SoD) policies are now generated and applied to agents that prevent agents from performing conflicting actions within the same workflow and enforce the same SoD controls that govern human actions.
Security teams: Identify and block unintended actions, such as a deadly trifecta, where an agent’s individually authorized sequence of actions produces an outcome that no one has authorized.
LangGuard platform built on Databricks. Supports any agent harness.
The LangGuard platform consists of three key components and is delivered as a single integrated product. LangGuard GRAIL Data FabricTM is a basic action system. The SCOPE-MCP server maps the reachable range of agents at design time. LangGuard Arbiter© enforces what they can. Together, they bridge the runtime permission gap, from visibility into agent action aspects to enforcing actions in real-time.
14 Days of F******** Offer at Databricks Data & AI Summit
Starting June 15th, LangGuard will offer current Databricks customers a 14-day f******* of the entire LangGuard platform. Data and AI teams can deploy LangGuard as an app in their Databricks workspace and work with their favorite agent harnesses within minutes.
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