As AI agents become primary consumers of enterprise data, lakeFS gives every agent run the isolation, reproducibility, and audit trail companies require
lakeFS, the control plane for AI-ready data, today introduced lakeFS for Agentic AI. This solution brings governed, reproducible data access to autonomous and headless agentic workloads operating at enterprise scale. lakeFS for Agentic AI extends capabilities already in production at organizations including Arm, Bosch, Lockheed Martin, NASA, Volvo, and the U.S. Department of Energy.
Enterprises are rapidly moving from AI pilots to agent-driven production workflows, where AI agents independently read, write, and transform enterprise data without a human reviewing every action. According to Dun & Bradstreet’s recent AI Momentum Survey, 97% of organizations report active AI initiatives, however, only 5% say their data is adequately ready to support them.
Agent Workloads Widen the Data Infrastructure Gap
In reality, agentic workloads make the data-readiness challenge much worse. Agents operate in parallel at machine speed across structured tables, unstructured files, images, video, and metadata, exposing the limits of manual governance and operational controls built for human-driven workflows.
lakeFS for Agentic AI addresses this directly. It gives every agent its own isolated data sandbox with a zero-copy branch of relevant data, validates and merges changes under policy, and produces a unified audit trail across every agent action.
“Agents are let loose on enterprise data at massive scale, but any agent that reads or writes to production data without isolation or a reproducible trail is a liability, no matter how good the model is,” said Einat Orr, CEO and Co-Founder of lakeFS. “The companies that win with agentic AI will solve this at the data layer and treat agents like production workloads, not experiments. That is what lakeFS has always done, and lakeFS for Agentic AI makes it explicit: the same control plane our customers use today is the one their agents need.”
Michael Simone, Senior Director Analyst at Gartner®, said*: “As autonomous AI agents become data producers and consumers, traditional manual stewardship cannot scale, making governance automation essential to handle the decision speed and timing that is required in agentic ecosystems.”
How lakeFS Data Sandboxing Creates a Trusted Foundation for Agentic AI
The core lakeFS technology that AI and data teams have been using at scale directly applies to autonomous agent workflows. lakeFS for Agentic AI is powered by its unique data version control architecture that provides zero-copy data sandboxing. It is built around the four pillars enterprises require before letting agents operate on production data.
- Isolation. Every agent works on its own zero-copy data branch, covering structured tables, unstructured files, and metadata together as one. Agent mistakes are automatically isolated and never corrupt production data. Recovery that used to take hours takes seconds.
- Reproducibility. Every agent run is tied to an exact, immutable version of the data. Past actions can be recreated, debugged, audited, or extended using the same inputs.
- Governance and compliance by design. Production data is gated by policy. Merges into production happen only after pre-merge validations pass. Every change can carry agent identity, run ID, and execution context. The result is a unified audit trail instead of evidence scattered across orchestrators, model providers, and cloud logs.
- Agent-native infrastructure. Agents read and write through standard file operations. lakeFS provides file-level data access with branch-scoped credentials that confine each agent to its own workspace. That keeps each agent’s working set narrow and avoids context bloat. No custom MCP server, SDK, or specialized integration is required.
Aansh Shah, Founder and CEO of Briefcase AI, said: “When AI systems act on private information, you need to know exactly what happened, when, and why. When building Briefcase AI it was critical for me that these controls live at the data layer and not be bolted onto the agent layer after the fact. lakeFS provides that foundational data layer for agentic AI, isolating every agent, letting you replay any run exactly, and proving what happened. That is what agentic AI demands of data infrastructure.”
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