Is Oracle AI Agent Studio the Right AI Tool for Oracle Fusion Clients?

by Kelly Wilkinson, Associate Partner at Centroid

Oracle’s pivot to AI over the last two years comes at an exciting time for organizations running Oracle Fusion applications. The potential is real: AI can help automate work, speed up decision-making, and make it easier for business users to create value.

At the same time, many Fusion clients are asking a very practical question: If we already run Fusion, is Oracle’s Fusion AI Agent Studio framework automatically the right fit for our use case?

That is a fair question and the answer is: sometimes, but not always.

As with most business automation initiatives, the right tool depends on the outcome you are trying to achieve. At Centroid, we have been prototyping common client use cases in our Fusion R&D demo lab to better understand where Oracle’s embedded AI Agent capabilities and AI Agent Studio fit best. Those prototypes reinforce a simple point: Fusion’s embedded AI and AI Agent Studio can work well for certain focused use cases, but they also have important limitations that matter when the use case becomes more complex.

That means Fusion clients should not start with the tool. They should start with the use case, the business outcome they want, and the level of accuracy, scale, and complexity the solution requires. In some cases, a custom AI Agent inside Fusion may be a strong fit. In other cases, the use case may require a broader architecture and a wider toolset.

Where AI Agent Studio can run into limits

From Centroid’s experience so far, the biggest challenges tend to show up in three areas: large data volumes, complex calculations, and more advanced workflow behavior. The documented limitations we observed include context window constraints, response truncation, API fetch limits, reduced accuracy for bulk or multi-step calculations, issues with long chat history, and workflow design constraints such as loop variable scope, sequential tool processing, and ability to email attachments

1. It is not designed for heavy data processing inside the LLM

One of the clearest limitations is what happens when too much data is pushed directly into the model. When large volumes of data are passed directly to the LLM, it can exceed context window limits, slow down performance, and produce incomplete or truncated responses.

For clients, that matters because many real business use cases are not small. A use case may sound simple at a high level — summarize transactions, review supplier activity, analyze order patterns — but the amount of underlying data can quickly become too large for the model to handle reliably in one pass. Based on the documented findings, this makes AI Agent Studio a better fit for narrow, scoped interactions than for broad, high-volume data analysis handled natively inside the LLM

2. Data retrieval limits can become part of the problem

The documented findings also note that REST API calls made through tools have a built-in fetch restriction: a default page size of 25 records and a maximum cap of 500 records per call.

That does not mean a solution is impossible, but it does mean data-heavy use cases need to be designed carefully. If a client expects an agent to review or reason across large sets of transactional data, those retrieval limits can affect how much the agent can practically process in a single flow.

3. LLMs are not the best choice for bulk or multi-step calculations

This is one of the most important takeaways. LLMs are generally fine for simple calculations, but accuracy drops significantly when they are asked to perform bulk or multi-step calculations. Oracle AI Agent Studio’s Calculator Tool is the better option where reliable numerical results are required.

That is a big distinction for business use cases. If the answer needs to be exact, repeatable, and dependable, such as totals, comparisons, status calculations, scoring logic, or other deterministic outputs, the LLM should not be the only engine doing the work. Put simply: language models are good at generating responses, but they are not the same thing as a calculation engine.

4. Long conversations can affect consistency

Another practical limitation comes from token usage and chat history. The model works within a fixed context window, and once that limit is reached, earlier context can be lost, which can lead to hallucinations or responses terminating unexpectedly.

The AI Agent also keeps adding prior exchanges into the running chat history and includes that history in each new call. Because of that, prompts are not always handled in isolation, and some responses may not update the way users expect.

For casual users, the easy way to think about this is: the longer and more complicated the conversation becomes, the greater the chance the agent may lose track or behave less consistently.

5. More advanced workflow patterns can become brittle

The documented limitations also show that workflow complexity matters. Tool calls are processed one at a time in sequence, and tasks that require multiple tools at once can affect the desired output.

There are also design constraints inside workflow patterns. For example, values assigned inside a loop are limited to that loop iteration and not available outside it, which can break downstream logic. The same findings note that processing individual records tends to behave more reliably, while decisions become less predictable as record counts and evaluations increase.

In plain English: simpler flows are safer; more complex, high-volume flows need more caution.

6. Communication limits

The workflow agent’s email node can only send 25 records per page and does not currently support sending attachments. That limits how much information the agent can share with end users in a single email. For large data volumes, pagination can be used as a workaround, but it is not ideal.

7. Data retrieval limits with conversation-style agents

Attachments uploaded in chat are not saved to a persistent content repository. They only exist within the agent’s execution context, so there is no external reference (for example, a UCMID) that REST APIs can use to retrieve them. Instead, the agent must read and interpret the file directly from the in-memory context during that session. Oracle has indicated this area will improve in future releases, which should enable agents to better read and use unstructured data from chat attachments.

So when is AI Agent Studio a good fit?

Based on Centroid’s prototyping (in Release 26A), Oracle’s embedded AI and AI Agent Studio appear to be best suited for specific, simple, and well-bounded use cases — especially those centered on guided interactions, focused automation, and user assistance inside Fusion.

Where clients can run into trouble is when they assume that because the capability is inside Fusion, it must also be the best answer for any AI requirement around Fusion. That is not always the case, especially where the use case depends on scale, deterministic processing, or more sophisticated orchestration.

The real decision point: start with the outcome

The most important lesson is straightforward: the right AI tool should be chosen based on the outcome required, not just on where the data lives.

If the goal is a focused, user-facing AI experience inside Fusion, a custom AI Agent built in AI Agent Studio may be the right answer. If the goal involves large-scale data processing, exact calculations, or more complex workflow logic, organizations may need a broader set of tools working together.

Final thought

Oracle’s AI momentum is creating real opportunity for Fusion clients, and that is a good thing. But success depends on matching the use case to the right tool. For some needs, AI Agent Studio may be a strong fit. For others, clients are better served by stepping back, clearly defining the business outcome, and choosing an architecture that can support it.

Centroid is helping clients apply Oracle’s AI capabilities across the full stack, including OCI and AI Data Platform–based solutions. If your organization has specific AI problems to solve, reach out to us for a complimentary consultation to explore how we can help you evaluate the right approach and tools for your use cases.