Welcome
UnO Agentic AI Builder
LLM/SLM Profile Library
Google Vertex Profile
Use the Google Vertex Profile to configure and manage the connection between HCLUnO Agentic AI Builder and the Google Vertex AI platform. This allows your agents to securely access and utilize Google's powerful foundation models, such as the Gemini family, for advanced generative and predictive tasks.

What's new
HCL Universal Orchestrator is constantly evolving to offer you the most comprehensive orchestration capabilities. For known limitations, workarounds, and contact information of HCL Customer Support, see Release notes.
What is HCL Universal Orchestrator
HCL Universal Orchestrator is a cloud-native workload orchestration and scheduling platform built on Kubernetes. Its microservices-based architecture enables the centralized orchestration of multi-cloud and hybrid environments from a single point of control.
Deploying and Administrating
All the information you need to deploy and administrate HCL Universal Orchestrator.
Using the UI
The UI is the HCL Universal Orchestrator interface, from which you can design your scheduling environment and monitor every process.
Orchestrating
Orchestrate anything with HCL Universal Orchestrator.
Orchestrating from Mobile
You can use your mobile devices to manage quickly and easily your whole HCL Universal Orchestrator environment.
Monitoring
Take your environment under control.
Using REST API
Use REST APIs to easily integrate workload scheduling capabilities with external products and solutions.
Administering multitenancy for service providers
Configure and administer a HCL Universal Orchestrator multitenant environment.
Uno AI Pilot
Welcome to the HCL Universal Orchestrator AI Pilot documentation. This resource is designed to guide you through everything you need to know about using UnO AI Pilot effectively. You will find detailed instructions, feature overviews, and helpful tips to ensure you get the best experience with UnO AI Pilot.
UnO Agentic AI Builder
- What's new
- What is UnO Agentic AI Builder?
- Installation and Configuration
  This section guides you through installing the UnO Agentic AI Builder.
- Fundamental Concepts
- UnO Agentic AI Builder Components
- Credential Library
- LLM/SLM Profile Library
  - AWS Bedrock Profile
    Use the AWS Bedrock Profile to configure and manage the connection between HCL UnO Agentic AI Builder and the foundation models hosted on Amazon Bedrock.
  - Azure Profile
    Use the Azure Profile to configure and manage the connection between HCL UnO Agentic AI Builder and Azure OpenAI Service. This integration enables your agents to leverage enterprise-grade OpenAI models (such as GPT-4, GPT-3.5-Turbo) hosted securely within your Azure environment.
  - Google Vertex Profile
    Use the Google Vertex Profile to configure and manage the connection between HCLUnO Agentic AI Builder and the Google Vertex AI platform. This allows your agents to securely access and utilize Google's powerful foundation models, such as the Gemini family, for advanced generative and predictive tasks.
  - Ollama Profile
    Use the Ollama Profile to configure the connection between HCL UnO Agentic AI Builder and your self-hosted Ollama service. Ollama is an open-source framework that allows users to run various Large Language Models (LLMs) locally or on a private network, enabling agents to leverage specific, highly customizable, and often smaller models.
  - OpenAI Profile
    Use the OpenAI Profile to configure the connection between HCL UnO Agentic AI Builder and the OpenAI API. This provides your agents with secure access to state-of-the-art foundation models like GPT-4, enabling advanced reasoning, complex content generation, and structured data tasks.
  - Nvidia Profile
    Use the Nvidia Profile to configure and manage the connection between HCL UnO Agentic AI Builder and the high-performance AI Foundation Models and microservices available through the Nvidia platform. This profile enables your agents to leverage models optimized for speed and scale in enterprise environments.
- User Guide
- Tools Library
- Templates Library
- Best practices to Create Agents
- Troubleshooting UnO Agentic AI Builder
- Known Limitations
  This section outlines known limitations and constraints of the current release, helping users understand the boundaries of supported functionality and avoid unintended usage scenarios.
Troubleshooting
Recover your environment if any issue occurs.
Notices

Google Vertex Profile

Use the Google Vertex Profile to configure and manage the connection between HCLUnO Agentic AI Builder and the Google Vertex AI platform. This allows your agents to securely access and utilize Google's powerful foundation models, such as the Gemini family, for advanced generative and predictive tasks.

Before you begin

You must have an active Google Cloud Project with the Vertex AI API enabled.
You must have a valid Google Credential configured in the Credential Library to authenticate this connection.
Ensure that all mandatory fields (marked with *) are completed accurately.

Table 1. Mandatory fields
Option	Description
Name	A unique identifier for this configuration instance. This name will be used to reference this specific Google Vertex model setup in the Agentic AI Builder.
LLM Name	The specific authentication credential (previously created in the Credential Library) used to authorize the connection to your Google Cloud Project.

Table 2. Optional fields
Options	Description
Model Name	The technical name of the model to use (for example, `gemini-2.5-pro`, `gemini-2.5-flash`). Note: The Model Name field is where you specify the exact technical identifier for the Large Language Model (LLM) or Small Language Model (SLM) to be used. If Discover Models is checked in the corresponding Credentials account, available LLM models will populate a dropdown menu for selection. If unchecked, the model list is not populated, and you must manually enter the model's exact technical name or identifier into the field.
Max Output Tokens	The maximum number of tokens the model is allowed to generate in the output response.
Temperature	The sampling temperature to use. Higher values mean the model will take more risks and generate more creative or varied output.
Top P	The cumulative probability of token selection for nucleus sampling. It limits the token choices to the most probable set based on this threshold.
Top K	The number of highest probability vocabulary tokens to keep for top-k filtering. The model samples from the $K$ most likely tokens for the next word.