Using AI agents in embedded enterprise applications can help organizations add automation and efficiency. But there are other, less obvious opportunities as well. Taking advantage of AI agent development platforms can give you the ability to tailor agents to your company’s specific needs—or create entirely new automation workflows. More specifically, by selecting an AI agent development platform that is “no code” or “low code,” like Oracle AI Agent Studio, you can expand the population of AI agent developers beyond highly technical staff. This opens the door to accelerated agent deployment and tighter alignment between business needs and enabling technology.
Agents as applications
AI agents can be thought of as mini applications. They take inputs, process them, and offer useful information in response or trigger a transaction, often in an external system. To date, developing and deploying agents has required significant time and software programming expertise. But now development platforms like Oracle AI Agent Studio make it possible to quickly define process flows and logic visually, then test and deploy working solutions without writing a single line of code.
No-code development platforms allow super users, rather than just specialized engineers, to create and deploy AI agents. Super users are technically adept business domain experts who manage the configuration, training, and first-tier support for applications. Visual interfaces and templates can significantly lower barriers to access and proficiency, giving super users the autonomy to address many of their own needs and make process changes quickly and without waiting for IT or long development cycles. The result is a faster pace of experimentation and solution validation, helping companies adapt with minimal delay to new requirements, regulations, or customer feedback.
As super users update or modify AI agents themselves, organizations can maintain better alignment between technology and evolving business priorities. While the long-term impact still depends on effective governance, no-code AI agent development environments are expected to make automation more accessible, responsive, and relevant to day-to-day business operations.
What about the complicated tech considerations?
While this all sounds well and good, anyone with enterprise apps experience knows there’s more to it. And there is. Previous attempts to open up application development to the masses hit two main stumbling blocks: security, and application integration. Let’s see how AI Agent Studio addresses these.
Secure at source
AI Agent Studio relies on Fusion Apps’ role-based access control (RBAC) security model. RBAC governs users’ access to data and application functions based on a set of privileges that are assigned once, then universally applied across Fusion Apps. This has two practical implications. First, in AI Agent Studio, users creating agents can only see, edit, or create data for which they have already been granted access. And second, once agents are deployed, RBAC automatically grants (or denies) end users access based on their existing privileges in Fusion Apps. In other words, no matter who builds an agent, they can’t inadvertently give users access to sensitive information they shouldn’t have.
Application integration
Integrating custom applications normally requires programmers (at any level of proficiency) to use APIs to connect their apps with existing systems and data sources. This involves some hard-core skills: making REST calls, understanding failure codes and managing contingencies, and handling JSON or some other flavor of payload structures. In AI Agent Studio, agents can access data through business objects, which allow them to get or store data about Fusion Apps entities via an extrapolated layer. The agent’s creator just designates which business object the agent needs from a menu of available options. Similarly, AI Agent Studio allows users to build connectivity between agents and external, agent-based resources using standards-based integration frameworks like the Model Context Protocol and the Agent2Agent protocol.
In conclusion
Oracle AI Agent Studio isn’t just about what gets built, but also about who can build it and who gets access. By handling some of the previous obstacles to wider participation in applications development, AI Agent Studio makes it possible for more super users to try extending Fusion Applications functionality to meet their specific, unique needs. This can promote an accelerated, more nimble Fusion Apps deployment, helping to create tighter alignment between business needs and enabling technology.
Related posts you might like
- New—Oracle AI Agent Studio for Fusion Apps
- Get started guide—design and build AI agent teams
- See Oracle AI Agent Studio in action (demo video)
If you’re an Oracle customer and want to get new stories from The Fusion Insider by email, sign up for Oracle Cloud Customer Connect. If you’re an Oracle Partner and want to learn more, visit the Oracle Partner Community.