Using Agent Studio Agent Studio provides a low-code to high-code environment to build, test, and deploy multi-agent workflows for generative AI applications. Agent Studio OverviewCloudera AI Agent Studio is a versatile low-code to high-code platform for building, testing, and deploying multi-agent workflows. Key features of Agent StudioCloudera AI Agent Studio capabilities include low-code workflow orchestration, custom tool extension, model integration, and Phoenix observability for production-ready AI agents.Use cases of Agent StudioAgent Studio provides the orchestration environment for building goal-oriented AI systems that automate multi-step workflows, including DevOps, data processing, and compliance screening.Launching Agent Studio within a ProjectYou can launch Agent Studio to build, test, and deploy multi-agent workflows.Deployment of Agent Studio using the ML runtime imageCloudera AI Agent Studio is now delivered as a prebuilt, containerized ML runtime image, replacing the previous method of building environments from raw source code on custom infrastructure.Migration to ML runtime modeTransition to Agent Studio ML runtime mode from the legacy Accelerators for Machine Learning Projects (AMP) deployments requires a fresh installation and manual data migration.Migrating WorkflowsThis section details the steps to transfer a workflow template between Cloudera AI Agent Studio instances, covering both export from the source and import into the target instance.Supported inference service providersEnterprise inference service providers connect with Agent Studio to facilitate Large Language Model (LLM) operations and API calls.Security recommendations for Agent StudioSecure the Agent Studio security framework by deploying the containerized ML runtime image and configuring strict project access controls.Agentic workflowsAgent Studio enables the creation of complex agentic workflows, which integrate multiple agents and tools, often using manager agents to orchestrate the entire process. Secure Tool Execution and DevelopmentSecure Tool Execution and Development is a framework designed to mitigate security risks such as credential exposure and data leakage by running user-defined tools in isolated environments rather than a shared runtimeAir-gapped EnvironmentsCloudera Agent Studio is now delivered as a prebuilt, containerized ML Runtime Image, replacing the prior method of building environments from raw source code on custom infrastructure. Service Account Usage in Agent StudioAgent Studio utilizes the Service Account feature to deploy production or shared workflows, using a dedicated Service Account instead of a user's account. This allows for fine-grained permissions, limiting the access of the deployed agentic workflow.Register models in Agent StudioManage Large Language Models (LLMs) within Agent Studio, by registering new models, the supported model providers, and by validating already registered models for proper functioningManaging user access with Role-Based Access Control (RBAC)Agent Studio includes a comprehensive Role-Based Access Control (RBAC) system to ensure secure access to workflows, models, and tools.This system allows for fine-grained control over who can view, edit, deploy, and delete resources within the agent studioManaging Workflow EvaluationsThe Evaluations feature offers a comprehensive set of diagnostic and quality-assurance tools designed to measure the performance, accuracy, and safety of your Agentic Workflows. This feature serves two main purposes, assessing workflows during the development phase within AI Studio, and enabling you to audit historical workflow runs in deployed workflows.Deploying workflows as model endpointsThe Cloudera AI Agent Studio's deployment system enables you to transform AI workflows into production-ready endpoints. When deployed, each workflow operates as an independent service, leveraging both Cloudera AI Workbench models and a Cloudera AI Workbench application. For more information see, Models overview Models overview and Analytical Applications Analytical Applications.Model Context Protocol (MCP) integration guideMCP enhances AI workflows by enabling seamless integration with external systems and tools. It provides a standardized approach for secure communication, allowing AI agents to interact efficiently with diverse services while maintaining flexibility and control within Cloudera AI Agent Studio.Monitoring feature in Agent StudioLearn about the Monitoring feature in Agent Studio and its integration with the Phoenix observability tool, to provide deep insights into workflow execution.Using inbuilt toolsTools in Agent Studio serve as the foundational building blocks for AI agents and workflows. They enable agents to interact with external systems, such as databases and APIs, and perform tasks that are beyond the capabilities of a Large Language Model (LLM) alone.
