First Look at Oracle AI Agent Studio.

A Platform-Level Development in Enterprise AI for Banking and Insurance.

AI agents — once primarily a research concept — are now being integrated into enterprise systems. However, while use cases are expanding, practical deployment within regulated financial institutions remains limited. Issues of governance, security, and data integration have all posed significant barriers.

Oracle AI Agent Studio addresses these constraints. Instead of positioning agents as bolt-on assistants or chatbot interfaces, Oracle has integrated them within its Fusion Applications suite — the same systems used to manage finance, HR, supply chain, and operations in many global banks and insurers.

This is our initial perspective on Oracle AI Agent Studio, how it operates, and its potential significance for the future of enterprise automation. A more in-depth technical and comparative review will follow.

Disclosure: This blog was created based on a conversation (or series of prompts) with Revvy, a ChatGPT developed by Revvence and trained on finance transformation and systems change-related content.

What is an AI Agent?

AI agents differ from LLMS and chatbots. They are systems that can:

• Understand a goal and plan the steps to achieve it
• Use tools and access enterprise data sources
• Make decisions with varying levels of autonomy
• Collaborate with other agents or hand off to humans
• Learn or refine behaviour through interaction

In short, an agent is not just something you prompt; it’s something you delegate to.

Looking ahead, it’s conceivable that managers may be responsible not only for teams of people but also for teams of AI agents tasked with carrying out operational processes.

What Is Oracle AI Agent Studio?

AI Agent Studio is a native component of Oracle Fusion Applications. It provides business and operations teams — not just developers — the ability to:

• Configure and enhance existing Oracle-built agents
• Build new agents using prebuilt templates
• Coordinate multi-agent workflows with optional human oversight
• Select from optimised LLMs or integrate a preferred model
• Extend agent functionality to third-party systems
• Validate and test agents before deployment
• Maintain full alignment with existing Fusion security roles, data permissions, and business rules

This last point is crucial: Oracle’s agents operate within the system of record, utilising the same data, workflows, and controls already in place. They are not external automations retrofitted onto the enterprise from outside.

What can you actually build?

Oracle provides a catalogue of templates aligned with common business domains (finance, HR, supply chain, customer service). Some templates involve single agents for routine tasks, while others model more complex workflows that involve multiple agents coordinating over time.

For example:

• An HR agent might respond to employee questions about policy or benefits, pulling from internal documents and HR records.
• A maintenance agent might orchestrate triage, diagnostics, and procurement based on sensor data and historical service records.
• A close cycle agent might monitor reconciliation progress and flag delays to ensure timeline integrity — without re-architecting the process.

The key enabler is that these agents can trigger native Fusion actions (e.g., update a record, launch a workflow, send an approval) and operate under the same governance model already defined for human users.

Why It Matters to Financial Institutions.

In banking and insurance, three platform features stand out:

1. Native Access to Business Logic and Data

Agents can interact with structured enterprise data (e.g., ledgers, capital models, risk reports) using Oracle APIs and business objects. There is no need for custom connectors or data extraction pipelines; the logic resides where the work happens.

2. Inheriting Security and Compliance Controls

Instead of creating new layers of oversight, agents operate with the same role-based access controls, approval structures, and data permissions that apply to human users. For regulated institutions, this minimises risk and speeds up deployment.

3. Workflow-First Orientation

Agents are not limited to conversational use cases. They are designed to execute steps in real business processes, including coordination with other agents, task scheduling, and optional checkpoints for human validation.

How does it compare?

Most other agent frameworks — such as Microsoft Copilot Studio, Amazon Agents for Bedrock, and OpenAI’s Assistants API — require the creation of integration layers to access enterprise systems. These platforms prioritise developers and are designed to operate outside existing workflows.

Oracle’s approach is the inverse: it starts inside the system of record.

In short, Oracle is not targeting general-purpose AI experimentation — it’s focused on operationalising AI within core business systems.

Revvence View.

This is a platform-level capability relevant to enterprise finance, risk, and sustainability teams — particularly in institutions already utilising Oracle Fusion.

It’s not a universal AI agent solution. However, for teams looking to integrate intelligence into established workflows — utilising existing data models, permissions, and auditability — Oracle AI Agent Studio represents a promising step forward.

We’ll be publishing a deeper review shortly, including:

• Technical deployment considerations
• Enterprise use case blueprints
• Comparative insights on Microsoft and Amazon approaches

How can we help?

Revvence can help in several valuable ways:

• Check out Revvy, our Narrow-GPT for Finance Transformation. Read all about Revvy here.
• Review one of your existing finance processes to recommend where AI capabilities will have the most impact.
• Conduct one of our Innovation Labs (a free three-hour workshop) to show you the art of the possible and help you build your business case for change.
• The design and delivery of end-to-end solutions.