AGENTIC INFRASTRUCTURE

The data layer for AI agents

LakeSail gives agents a governed system for execution, validation, and adaptive branching. Run Python and SQL, define tools dynamically, and operate safely on your data at scale.

Get Access Talk to us →
Capabilities

Built for agentic workloads

The core infrastructure agents need to execute, validate, and operate safely on data at scale.

Elastic Agent Compute

Provision compute on demand for each agent workload, scale with execution, and release resources when the work is done.

Dynamic Tool Creation

Agents can define custom Python data sources and UDFs at runtime, giving them a flexible execution layer that adapts as work unfolds.

Governed Execution

Every workload runs with auditable controls, clear isolation boundaries, and human oversight built in from the start.

A Shared Data Layer

Agents operate on structured, queryable data instead of scattered context and files, making execution more reliable, observable, and governable.

Automatic Lakehouse Branching

Create isolated lakehouse branches automatically so agents can explore, validate, and recover safely without touching production data directly.

Native Python Execution

Python executes directly inside the engine via PyO3, with zero-copy access to shared Arrow buffers for high-performance agent workloads.

Why LakeSail

Legacy data engines weren’t built for agents

Agents need fast startup, dynamic Python execution, safe branching, and governed access to data. Traditional JVM-based systems were built for a different model of work.

Traditional JVM Platforms
Startup Model
Heavier runtime overhead
JVM startup, warm-up, and executor overhead before work begins
Python Execution
Cross-process execution
Python runs outside the engine process, adding serialization and IPC overhead
Branching and Validation
Not built for agent workflows
Agent-style validation, recovery, and adaptive execution require additional systems
Execution Model
Cluster-oriented
Long-lived infrastructure designed for traditional batch and ETL workloads
Cost Model
More idle overhead
Keeping infrastructure warm can add cost even when workloads are intermittent
LakeSail
Startup Model
Fast, lightweight startup
Rust-native engine with no JVM or VM warm-up path
Python Execution
In-process, zero-copy execution
Python runs inside the engine via PyO3 with zero-copy access to shared Arrow buffers
Branching and Validation
Built for agent workflows
Automatic lakehouse branching supports exploration, validation, and safe recovery
Execution Model
Elastic and workload-driven
Compute provisions per workload, scales with execution, and releases when work is done
Cost Model
Scale-to-zero economics
You pay for active compute instead of keeping idle clusters running
Agents need a governed execution layer for data. LakeSail is built to make agent workloads operational, auditable, and safe at scale.
Getting Started

Simple to get started

From signup to running agent workloads in four steps.

1

Create Account

Sign up with email, verify via code, and set up mandatory 2FA.

2

Connect AWS

Launch a CloudFormation template in your account. Requires admin access.

3

Connect Your Agent

Connect your agent workflows to LakeSail and start running governed workloads on your data.

✓

Run Agent Workloads

Agents run SQL and Python, define tools dynamically, and operate safely on governed data.

Native Python
Runs in-process on the Rust engine
0
Cross-process Python serialization overhead
Elastic
Compute provisions per workload and scales to zero

Give your agents a data layer built for production

Get Access Talk to us →