OpenAI-compatible endpoints power agents and copilots on private models, with guardrails enforced on every step of every agent loop.
Agentic AI uses language models to plan, act, and iterate — calling tools, retrieving documents, and producing multi-step outputs. When this runs on a hosted API, every intermediate result, every document chunk, and every tool response passes through a third party. Private agentic AI runs the same workflows on infrastructure you control, with OpenAI-compatible endpoints so existing agent frameworks need no code changes.
Every tool call, every document chunk, every intermediate result in an agentic workflow passes through a hosted API. At scale, your proprietary workflows, documents, and IP become training data for the provider.
Private inference with OpenAI-compatible endpoints, so agent frameworks like LangChain, AutoGPT, and custom agents repoint to your infrastructure with no code changes.
Hardware TEE isolation for agentic workloads that require the highest assurance of IP protection.
Explore Cocos AIPrivate agentic AI is the deployment of autonomous AI agents — systems that use a language model to plan, take actions, retrieve information, and produce multi-step outputs — on infrastructure you own, so that every tool call, document retrieval, and intermediate result stays inside your network. No interaction with the agent loop passes through a hosted API or a third-party server.
Agent frameworks like LangChain, AutoGPT, CrewAI, and custom agent loops communicate with a language model via an OpenAI-compatible API. Cube AI exposes this same API format, so you change the base URL and API key in your agent configuration to point to your on-premises Cube AI instance. No code changes are required in the agent framework itself.
In a typical agentic workflow on a hosted API, every component of the loop is transmitted: the original user query, each tool call and its response, every document chunk retrieved from your knowledge base, all intermediate reasoning steps, and the final output. Over many interactions, this exposes your proprietary documents, workflows, and operational data to the provider's infrastructure.
In Cube AI, NeMo guardrails are applied on the inference path — not at the application layer. Every prompt sent to the model and every response returned passes through the guardrail logic before reaching the agent. This ensures policy enforcement cannot be bypassed by crafting inputs that manipulate the application-layer filtering, and it applies consistently across every agent loop iteration.
Yes. Cube AI includes integrations for coding agent frameworks including Continue and OpenCode. These editors connect to Cube AI's OpenAI-compatible API, so code completions, inline suggestions, and multi-file edits are generated by a model running on your own infrastructure. No source code is transmitted to a third-party provider.
Private AI describes where inference runs — on your own infrastructure. Agentic AI describes how the model is used — in an autonomous loop that calls tools, retrieves documents, and acts across multiple steps. Private agentic AI combines both: the agent loop runs entirely on private infrastructure, with no external API calls at any step.
Talk to the team about agentic AI deployments, OpenAI-compatible endpoints, and coding agent integrations.
