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Agentic Runtime

A governed execution environment for deploying, operating, and scaling AI agents across enterprise systems, data, and workflows.

TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE
The Problem

Building Agents Is Easy. Operating Them Is Hard.

Modern AI agents are being built across LangGraph, CrewAI, AutoGen, OpenAI SDKs, and custom frameworks. But enterprises still struggle to operate them safely.

Most agent frameworks stop at development. Very few platforms solve how agents operate safely inside enterprise environments.

Enterprises still lack:
Deployment
Governance
Sandboxing
Access control
Observability
Runtime isolation
Scaling
LangGraph
CrewAI
AutoGen
OpenAI SDK
LlamaIndex
Custom
Platform Approach

One Runtime for Enterprise AI Agents

Agents can be developed anywhere, uploaded into the runtime, and deployed rapidly across enterprise environments—without custom operational infrastructure.

Unified Execution Runtime
One operational layer for agents built in any framework—no custom infrastructure required.
Centralized Governance
Identity-aware policy enforcement across every agent, tool, and data system.
Sandboxed Execution
Isolated runtime environments with controlled permissions and policy-aware boundaries.
Scalable Deployment
Deploy rapidly across cloud, on-prem, and hybrid—without operational re-platforming.
Sandboxed Execution

Secure Sandboxed Runtime for AI Agents

Every agent executes inside isolated runtime environments with enterprise-grade security boundaries—across cloud, on-prem, and hybrid.

Every agent runs with
Controlled permissions
Governed data access
Runtime isolation
Policy-aware execution
Enterprise-grade security boundaries
Governed Access · xStore

Centralized Governance Across Agents and Data

The xStore governance layer provides centralized access control, policy enforcement, and identity-aware execution across every agent and system.

Agents
Users
Tools
Data Systems
Infrastructure

Agents inherit permissions, governance policies, and access boundaries—without custom security implementations.

Unified Runtime

Where Data Runtime Meets Agent Runtime

One coordinated runtime architecture unifying analytical execution, distributed data processing, and autonomous agent execution.

Processing
Spark
Large-scale data processing and AI feature engineering.
Analytics
Trino
Interactive SQL across federated enterprise data.
Autonomy
Agent Frameworks
Governed execution of autonomous and multi-agent workflows.

Inside one operational platform, agents can

Query governed enterprise data
Trigger workflows
Orchestrate systems
Execute actions safely
Autonomous Applications

Powering Autonomous Enterprise Applications

Reason over enterprise data, interact with systems, execute governed actions, and continuously improve—without fragmented infrastructure.

AI Copilots
Embedded assistants grounded in governed enterprise context.
Autonomous Analytics
Agents that observe, decide, and act on enterprise data continuously.
Operational AI Assistants
Domain-aware assistants integrated into enterprise workflows.
Multi-Agent Systems
Coordinated agents executing complex enterprise processes.

Outcomes

What changes when analytical execution becomes a single runtime.

Rapid deployment of enterprise AI agents
Unified governance across agents and data
Secure sandboxed execution
Faster AI application delivery
Centralized operational control
Autonomous workflows operating safely at scale
Reduced operational complexity

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