Oracle announced Oracle AI Agent Studio1 for Industry Applications, a no-code and low-code environment for creating, testing, orchestrating, deploying, and managing AI agents and workflows for industry-specific business processes. Building on Oracle AI Agent Studio for Fusion Applications, the new offering enables customers and partners to build and extend agents, leveraging existing application data, tools, and business processes, and embed these agents into daily workflows to reduce manual effort and improve productivity.
"AI agents create value when they can work within the processes, data, and controls that define how an industry operates," said Simon de Montfort Walker, executive vice president, Industry Applications, Oracle. "Oracle AI Agent Studio for Industry Applications gives customers and partners the building blocks for those processes, along with the flexibility to adapt, test, and manage agents for their own needs. This approach helps organizations focus their time and effort on areas that will help them grow their business and serve their customers more effectively."
Building and operating AI agents around business outcomes
Oracle AI Agent Studio for Industry Applications helps accelerate agent development with prebuilt agents, workflows, templates, and reusable tools designed around industry-specific business processes. With the platform, customers and partners can adapt these capabilities to their business needs and configure agents around their own data, decisions, roles, and policies. Users can also test and debug agents before deployment and monitor execution, performance, and AI-unit consumption as adoption grows.
The platform provides customers and partners with:
Reusable industry foundations: Build upon prebuilt industry agents, workflows, templates, and tools to accelerate agent development and reduce the need to create each use case from scratch.
Natural-language and visual authoring: Create and extend agents using natural language instructions and visual workflow editing, including defining roles, goals, tools, policies, and operating context.
Cross-application orchestration: Coordinate supported agents, tools, and services across Oracle Industry and Oracle Fusion Cloud Applications to support multi-step business processes and workflows across applications.
Model Context Protocol (MCP) support: Connect agents to approved Oracle Industry Application tools and capabilities through MCP servers for reuse across multiple use cases.
Choice of generative AI models: Select from supported models available through Oracle Cloud Infrastructure (OCI) Enterprise AI based on the requirements of a particular agent or use case.
Testing and debugging: Test agent responses, actions, and tool use before deployment. Trace workflow execution, inspect inputs and outputs, evaluate results, identify issues, and retest changes.
Enterprise security controls: Apply guardrails and help ensure agents operate within configured permissions, policies, and organizational roles, while managing access to applications, data, and tools.
Deployment and lifecycle management: Move agents from development and testing toward production and manage them throughout their lifecycle, with deployment and runtime-management capabilities available for supported use cases.
Monitoring and observability: Monitor agent execution metrics, performance, reliability, usage, and AI-unit consumption to manage adoption with greater visibility and control.
"As organizations continue to operationalize AI across their enterprise, the challenge is AI agents are only as useful as the data, tools, and approvals they can reach, and connecting all three reliably is where most efforts stall," said Eric Newmark, GVP and General Manager of Enterprise Business Applications, IDC. "Oracle AI Agent Studio for Industry Applications goes after that friction directly. It lets teams build, test, and manage agents inside the industry workflows they already run. That is what turns adoption from a pilot into something practical, and something that scales."
