OCI Enterprise AI Features

A consistent approach for agents and generative AI

Build, orchestrate, deploy, and govern AI applications and agents in one managed OCI service.

A choice of leading foundation models

Access pretrained models from Cohere, Google, Meta, OpenAI, and xAI through a consistent API.

Agent orchestration with ready-to-use tools

Automate multistep workflows using built-in capabilities, such as retrieval-augmented generation (RAG), for grounded answers and natural language to SQL for structured data.

Support for standards-based open source frameworks and protocols

Build on existing skills and integrations while keeping options open as models and tooling change. Use agentic frameworks, such as LangChain, LangGraph, and AutoGen, with OpenAI Responses API compatibility, plus Agent2Agent (A2A) and Model Context Protocol (MCP) support.

Smart model routing across regions

Improve access to on-demand model capacity by routing inference requests across user-selected OCI regions where the model is available. Maintain control over where inference is processed while reducing dependence on capacity in a single region.

Run agents in isolated environments

The OCI Enterprise AI Agent Sandbox feature provides isolated, on-demand environments where AI agents can execute code and carry out tasks. Set resource, duration, and network-access limits while OCI manages the sandbox infrastructure.

Production deployment and performance options

Run enterprise workloads with managed deployment and dedicated AI clusters on private GPUs for stable, high-throughput performance.

Enterprise security and controls built in

Use identity and access management (IAM) integration, guardrails, auditability, and observability to deploy agents with confidence, including in regulated environments.

Operations and lifecycle management

Monitor usage and quality with analytics and moderation controls, and manage model endpoints over time to support production operations.