One of the challenges with talking about AI Agent Studio is that the name can be slightly misleading. People hear the word agent and immediately picture a chatbot, a large language model, or an automated assistant. Whilst those are certainly part of the story, they’re only a small part of it. After spending time exploring Oracle’s latest AI Agent Studio capabilities, I’ve become increasingly convinced that the most important thing Oracle is building isn’t actually the agents themselves. It’s everything around them.

Enterprise AI isn’t difficult because AI models are difficult. Enterprise AI is difficult because organisations need systems that are secure, governed, reliable, testable and capable of operating at scale. Oracle’s latest vision for Agentic Applications reflects this, positioning agents as just one layer within a much broader framework that includes orchestration, security, governance, testing, approvals, connectors and enterprise context. That’s a very different proposition from simply deploying a chatbot.

One of the concepts Oracle talks about repeatedly is the idea of Agentic Applications rather than individual agents. An individual agent can reason, recommend and generate content. An Agentic Application brings together agents, workflows, data, approvals, security controls and user experiences to deliver a specific business outcome. Oracle describes these applications as teams of specialised agents working together to achieve business objectives and deliver measurable outcomes. I think that’s an important distinction.

Most business challenges aren’t solved by a single person working alone. A recruitment process doesn’t rely entirely on HR. A supplier issue isn’t owned solely by Procurement. A financial review often involves multiple stakeholders bringing different expertise and perspectives. The same principle applies here.

Rather than building one all-knowing agent that attempts to handle everything, organisations can create specialist agents focused on particular domains, responsibilities or business processes. Oracle’s architecture explicitly supports specialised agents working as coordinated teams, allowing organisations to break complex business challenges into smaller, more manageable areas of expertise. That may sound like a technical design choice, but it’s actually a business one. The clearer an agent’s purpose, the easier it becomes to build trust, establish accountability and continuously improve performance.

Whenever I speak to customers about AI, I often encounter the assumption that there are only two types of people involved: business users and developers. The reality is far more nuanced. Oracle’s latest AI Agent Studio experience recognises this by providing a spectrum of builder experiences, ranging from no-code and conversational design through to full pro-code development using tools such as VS Code, command-line interfaces and source-controlled development workflows.

Business users can describe outcomes in natural language, whilst developers retain full control when required. I think this approach reflects how successful transformation projects actually work. The people closest to a business challenge are often best positioned to understand the outcome they’re trying to achieve. The people responsible for architecture, security, integrations and governance are best positioned to ensure solutions can operate safely and reliably at enterprise scale. Neither group can solve the problem effectively in isolation. By supporting different types of builders, Oracle appears to be acknowledging that enterprise AI is as much about collaboration as it is about technology.

One of the biggest differences between consumer AI and enterprise AI is context. Public AI tools are incredibly capable, but they generally know very little about your organisation. They don’t understand your policies, your data, your approval processes or your business rules. Enterprise AI becomes valuable when it can work within the context of the organisation it serves.

Oracle’s architecture places significant emphasis on enterprise knowledge, business objects, connectors, policies and organisational context. Agents can be grounded in enterprise content, connected to external systems, and informed by the processes and data that already exist within Fusion Applications.

Why does that matter? Because business decisions rarely rely on a single source of truth. An HR recommendation might depend on employee records, compensation history, learning activity and organisational policy. A procurement recommendation could depend on supplier performance, contract information, delivery schedules and financial exposure. A project management decision may require information from staffing, budget, risk and operational systems. The more context an agent can access responsibly, the more useful it becomes. Without context, intelligence quickly loses value.

One of the most interesting additions to AI Agent Studio is the introduction of Policy Models. This capability addresses a challenge that many organisations have been struggling with since generative AI entered the mainstream. What happens when a decision must be correct every single time? There are many situations where approximation simply isn’t acceptable. Benefits eligibility. Financial calculations. Regulatory compliance. Contractual obligations. Compensation rules.

Oracle’s answer is Policy Models. These allow organisations to upload source policy documents, generate executable policy functions, validate them using test cases and then invoke them within workflows as deterministic decision-making components. Policy logic becomes reusable, consistent and centrally managed rather than being recreated repeatedly across multiple workflows.

Personally, I think this is one of the most significant capabilities Oracle has introduced. Not because it’s particularly exciting. But because it addresses one of the most common concerns organisations have about AI. Trust. Many business leaders are comfortable allowing AI to summarise information or suggest recommendations. They’re less comfortable allowing AI to interpret regulatory rules or apply financial calculations inconsistently.

Policy Models create a clear separation between reasoning and rules. The AI can help understand the situation. The policy ensures the decision follows the organisation’s defined rules. I suspect that distinction will become increasingly important as organisations move from experimentation to production use cases.

One message came through strongly when reviewing Oracle’s latest direction for AI Agent Studio. Enterprise AI is moving beyond experimentation. Oracle talks about AI Agents becoming operational centres within deployable business applications, supported by trust, governance, integration, reliability and business context.

The focus is shifting from individual AI capabilities towards complete systems that can safely execute work and deliver outcomes. I think that’s exactly where the industry needs to go. Most organisations aren’t looking for another AI demonstration. They’re looking for solutions to real business problems. They want to improve employee experiences. Reduce risk. Accelerate decision-making. Increase efficiency. Improve service delivery. That’s not achieved through agents alone.

It’s achieved through combining intelligence, governance, data, approvals, security and business processes into a cohesive solution. And that’s what makes Oracle’s current direction particularly interesting. The conversation is no longer about whether AI can generate a response. It’s about whether AI can operate responsibly within the realities of an enterprise environment. For me, that’s where the real story begins.

In the next article, I’ll explore another critical aspect of enterprise AI: trust. Specifically, how testing, approvals, governance, security and auditability are helping organisations move from AI experimentation to AI they can confidently rely on every day.

Please note that all screenshots are the property of Oracle and are used in accordance with Oracle’s Copyright Guidelines.

In the first article in this series, I talked about how Oracle Fusion AI Agent Studio appears to be shifting the conversation away from technology and towards business outcomes. In the second article, I explored Oracle’s Builder Assistant and how it helps close the gap between an idea and a working solution. But at some point, it’s worth understanding what actually sits behind an Agentic App. Not because you need to become a developer or learn every technical detail. Quite the opposite. Understanding the building blocks helps explain why Agentic Apps have the potential to deliver value in a way that traditional dashboards, reports and workflows often struggle to achieve.

One of the biggest misconceptions I encounter is that an Agentic App is simply a chatbot with a different interface. After spending time working with Oracle AI Agent Studio, I don’t think that’s an accurate description at all. A well-designed Agentic App is more like a digital team. Different components contribute different capabilities, each performing a specific role whilst working together towards a common outcome. When you look at it through that lens, the architecture starts to make much more sense.

At the heart of every Agentic App are one or more agents. Oracle refers to these as workflow agents, but I think it’s easier to think of them as specialists. In any organisation, complex decisions rarely rely on a single person. Different experts contribute different perspectives. A manager dealing with an employee issue might seek input from HR, Payroll and Learning teams. A finance director reviewing project performance might need information from project managers, procurement specialists and budget owners. Agentic Apps work in a similar way.

Instead of trying to create one agent that knows everything, Oracle encourages organisations to build focused agents with clearly defined responsibilities. One agent might specialise in workforce compliance, another in employee retention, another in supplier risk and another in project performance. That may sound like a technical decision, but it’s actually a business one. The clearer an agent’s purpose, the easier it becomes to trust its recommendations, govern its behaviour and improve its effectiveness over time.

Most people don’t open business applications because they enjoy looking at data. They open them because they need answers. This is why insights are such an important part of the Agentic App experience. Rather than requiring users to navigate through reports, dashboards and transactions, agents can analyse information from multiple sources and surface the things that genuinely matter.

Think about an HR manager responsible for hundreds of employees. What they typically need isn’t more data. They need help identifying which individuals require attention today. Perhaps a valued employee is showing signs of disengagement. Perhaps a professional certification is about to expire. Perhaps a new starter hasn’t completed mandatory onboarding activities. An insight helps bring those situations to the surface before they become bigger problems.

The same principle applies across Finance, Procurement, Supply Chain and Customer Operations. The objective isn’t simply to provide information. It’s to help people focus their limited time and attention where it will have the greatest impact. That’s where the real value starts to emerge.

One of the challenges with traditional reporting is that the report often becomes the end of the process. A manager discovers there’s a problem. They make a note. They send an email. They open another application. They create a task. Eventually, something happens. Agentic Apps are designed to shorten that journey.

In Oracle’s architecture, agents can surface actions alongside the insights they generate. Rather than simply highlighting an issue, they can help the user decide what to do next and initiate the appropriate process. Imagine receiving an alert that an employee’s work permit is due to expire. The application could simply notify you. Or it could help initiate the follow-up activity, prepare communications, gather supporting information and guide you through the next steps.

Similarly, if a procurement agent identifies a supplier at risk of missing a key delivery commitment, the application could help trigger supplier engagement activities before the issue starts affecting customers. The outcome is the same. The difference is the amount of effort required to get there. I think that’s one of the most important distinctions between traditional productivity tools and agentic applications. They don’t just help people understand what needs attention. They help people move towards resolution.

Whenever I talk to customers about AI, one concern inevitably comes up. What happens when the system starts communicating on behalf of people? It’s a valid question. Most organisations want AI assistance, not AI acting independently without oversight.

What I particularly like about Oracle’s approach is that communications are designed to support people rather than replace them. AI can help draft content, prepare messages and bring together relevant information, but the human remains responsible for reviewing, approving and ultimately deciding whether communication should be sent.

Think about an HR team responsible for monitoring expiring visas or certifications. Creating individual communications can be time-consuming, especially when managing large populations. An agent can help prepare that content using the information already available, allowing HR professionals to focus on quality and decision-making rather than administration. The same principle could apply to supplier communications, project updates, audit notifications or customer engagement activities. The technology handles the repetitive work. People retain ownership of the decision. That feels like the right balance.

Data Still Matters. None of this works without data. However sophisticated the AI might be, the quality of the outcome will always depend on the quality of the information available. This is where Oracle’s business objects, integrations and data access capabilities play such an important role. Agents need access to information before they can generate meaningful insights, recommendations or actions.

The good news for Oracle Fusion customers is that much of that information already exists within the applications they use every day. Employee data. Talent information. Financial transactions. Supplier records. Projects. Learning activities. Procurement spend. The opportunity isn’t necessarily creating new information. It’s bringing existing information together in a way that helps people make better decisions.

Individually, agents, insights, actions, communications and data capabilities are all useful. Collectively, they become something more powerful. An insight identifies an issue. An action helps drive resolution. A communication engages the right people. An agent orchestrates the process. Underlying data provides the necessary context. Together, they create an experience focused on outcomes rather than transactions. And I think that’s really the key message behind Agentic Apps.

They’re not trying to replace Oracle Fusion. They’re not trying to replace employees. They’re not even trying to replace existing processes. Instead, they’re helping organisations navigate increasing levels of complexity by bringing together information, recommendations and actions in a more intelligent way. That’s a challenge every organisation faces, regardless of industry.

As organisations begin experimenting with Agentic Apps, it can be tempting to focus on the technology itself. Which model should be used? Which integrations are required? Which features should be enabled? Those questions are important, but they’re not the first questions I would ask. I’d start with the business outcome. What decision are we trying to improve? What problem are we trying to solve? What experience are we trying to create? Once those answers are clear, the building blocks start to fall naturally into place.

By now, we have explored the core building blocks that make Agentic Apps possible. However, if you’ve ever wondered what separates an interesting AI demonstration from something that can genuinely be trusted in a production business environment, that’s where the story becomes even more interesting. In the next article, I’ll look beyond the agents themselves and explore the capabilities that make enterprise AI possible at scale, from policy models and human approvals to enterprise integrations, governance and control.

Please note that all screenshots are the property of Oracle and are used in accordance with Oracle’s Copyright Guidelines.

In my previous article, I talked about how Oracle Fusion AI Agent Studio feels different from many of the AI platforms that have emerged over the last few years. The shift isn’t really about a new feature or a new model. It’s about moving away from technology-first thinking and focusing instead on business outcomes.

One capability in particular stands out as an example of that shift: the Builder Assistant. On the surface, the Builder Assistant looks like another AI-powered assistant. You describe what you want, it helps generate the building blocks, and you continue refining the solution through conversation. But I think the significance runs deeper than that.

For years, we’ve accepted that creating applications requires a detailed understanding of how technology works. We capture requirements, design workflows, build integrations and configure business logic, often translating the same business need several times before reaching a finished solution.

The Builder Assistant starts to challenge that assumption. Rather than asking users to begin with the technical design, it encourages them to start with what they are trying to achieve. That might seem obvious, but it’s a meaningful change in how applications are conceived and created.

When organisations begin any transformation programme, they rarely describe their challenges in technical terms. An HR Director doesn’t typically ask for a workflow containing multiple nodes, integrations and business objects. They might say: “We need to identify employees who may be at risk of leaving.” A Procurement Manager might say: “We need earlier warning when suppliers are likely to miss critical deadlines.” A Finance Director might say: “We want to focus our team’s attention on unusual spending patterns before they become a problem.” These are business challenges, not technical requirements.

Traditionally, there has been a considerable gap between those conversations and the resulting application design. Someone has to translate the business need into technical specifications before any development can begin. What Oracle appears to be doing with the Builder Assistant is helping to narrow that gap. Instead of immediately thinking about workflows, integrations and data structures, users can start by describing the outcome they want to achieve. The platform then helps build the underlying components needed to support that outcome. The complexity hasn’t disappeared, but it has become less of a barrier to getting started.

A common misconception about AI is that its primary purpose is automation. Whilst automation is certainly part of the story, I think one of the biggest opportunities lies elsewhere. AI can help organisations capture expertise more effectively. Every organisation has people who understand its processes, challenges and priorities exceptionally well. They know which risks matter, which decisions take too long and where valuable time is lost. What they often lack is the ability to translate those ideas into working applications. Historically, that has required specialist technical skills.

As tools like the Builder Assistant evolve, we’re starting to see a world where business experts can contribute much more directly to the solution design process. Imagine an HR specialist describing the characteristics of employees who may require additional support and seeing a prototype solution emerge. Imagine a compliance team defining the type of risks they need to monitor without having to understand every technical object behind the scenes. That doesn’t remove the need for governance, technical review or implementation expertise. But it does allow the people closest to the problem to become much more involved in shaping the solution. For many organisations, that could prove transformational.

One of the things that interests me most about Agentic Apps is that they encourage organisations to think differently about applications altogether. Traditionally, software has been designed around transactions and processes. Users navigate through screens. They search for information. They run reports. They review dashboards. Then they decide what action to take. Agentic applications take a different approach. They start with the question: “What does the user actually need to accomplish?”

A manager may not care about navigating between multiple screens to gather workforce information. They care about whether someone on their team requires support or intervention. A procurement leader may not care about reviewing dozens of supplier records. They care about understanding where the next potential supply chain issue is likely to emerge. A finance manager may not want another dashboard. They want confidence that significant risks or unusual patterns will be highlighted before they become larger problems. This shift from information-centred design to outcome-centred design is one of the most interesting aspects of the agentic journey. The Builder Assistant feels like an important step in enabling that change.

One challenge I hear repeatedly from customers is that they have far more ideas than they have capacity to deliver. Every organisation can identify opportunities to improve employee experiences, streamline processes or make better use of data. The difficulty is often turning those ideas into reality. Development backlogs grow. Priorities compete for attention. Useful ideas can remain stuck in planning documents for months or even years.

By reducing some of the complexity involved in creating solutions, tools like the Builder Assistant have the potential to accelerate innovation. That doesn’t mean every idea should become an application. Nor does it mean every solution should be built without proper governance or oversight. What it does mean is that organisations can spend more time evaluating whether an idea delivers value and less time struggling with the mechanics of getting started. That feels like a much healthier place to focus effort.

Whenever AI enters the conversation, there is often concern about replacing human expertise. Personally, I see the opposite happening here. The value of the Builder Assistant isn’t that it removes people from the process. It’s that it allows people to contribute in different ways. Business specialists can focus on outcomes. Functional consultants can focus on user experience, process design and governance. Technical teams can concentrate on architecture, security and enterprise integration. Each group can spend more time applying their expertise where it adds the greatest value. The technology becomes an enabler rather than the centre of attention. And that’s exactly how it should be.

We’re still at the beginning of this journey. The Builder Assistant is not going to eliminate the need for solution design, implementation expertise or governance. Complex enterprise applications will always require careful planning and oversight. But the direction of travel is becoming increasingly clear. The gap between having an idea and creating a working solution is reducing. The ability to express business intent is becoming just as important as technical skill. And organisations are starting to gain tools that help them focus on outcomes rather than implementation details. For me, that’s a far more interesting conversation than discussing individual AI features.

In the next article in this series, I’ll look at the building blocks that sit behind Agentic Apps and explore how agents, actions, communications and insights work together to deliver meaningful business outcomes.

Please note that all screenshots are the property of Oracle and are used in accordance with Oracle’s Copyright Guidelines.

I’ve spent quite a lot of time exploring Oracle Fusion AI Agent Studio over the past few months. Like many new Oracle capabilities, it’s evolved quickly. Features have appeared, interfaces have changed, and new possibilities seem to arrive with every release.

What struck me recently wasn’t a new large language model, agent type or connector. It was the user experience itself. The latest version of AI Agent Studio feels fundamentally different from where it started. That matters because one of the biggest challenges with enterprise AI has never really been the technology. It’s been accessibility.

For years, we’ve talked about empowering business users, citizen developers and functional consultants, yet many AI platforms still require specialist knowledge before you can create anything useful. Understanding prompts is one thing. Understanding workflows, orchestration, APIs and integrations is quite another. Oracle appears to be addressing that challenge head on.

Historically, building applications has required people to translate business requirements into technical specifications. A business leader explains the problem, a consultant documents the requirements, and a developer builds the solution. The process works, but it introduces delays, complexity and the possibility that something gets lost in translation along the way.

What’s interesting about the latest AI Agent Studio experience is that Oracle is increasingly allowing people to describe what they want to achieve rather than focusing on how every component should be configured. Instead of starting with technical objects, workflows and integrations, users are encouraged to start with an outcome.

• What problem are you trying to solve?
• What decision are you trying to support?
• What action should happen next?

That might sound like a subtle change, but I think it’s one of the most significant developments we’ve seen in enterprise software for some time.

For example, an HR team might want to identify employees at risk of leaving and ensure managers take proactive action before valuable talent is lost. A procurement team might want to highlight suppliers that are showing signs of delivery risk before those issues impact customers. A finance team might want to focus attention on unusual transactions that warrant further investigation. The desired business outcome is clear, even if the technology required to achieve it is complex. The technology still matters, of course, but it increasingly sits behind the outcome rather than being the starting point.

One of the themes I keep hearing from customers is that traditional applications were designed around processes. Employees complete forms, managers review transactions, reports provide information and dashboards highlight exceptions. While these tools are valuable, there is often a gap between understanding what requires attention and actually taking action. This is where agentic applications become interesting. Rather than simply presenting information, they can help identify issues, prioritise actions and guide users towards the next step.

Think about a manager reviewing their team. Traditionally, they might need to examine performance ratings, compensation information, employee engagement scores, absence trends and talent data separately before deciding where intervention is needed. An agentic application takes a different approach. It brings those signals together, highlights where attention may be required and presents recommendations in context.

Imagine opening an application and immediately seeing that a high-performing employee has received below-market compensation, has recently missed development opportunities and is showing signs of declining engagement. Instead of spending time gathering information from multiple sources, the manager can focus on deciding what action to take.

Similarly, a procurement leader could be alerted that a critical supplier has missed key milestones and may be putting future deliveries at risk. A finance manager might receive an early warning that project spending is trending above expectations long before budget thresholds are breached. An HR team could be notified about work permits, professional certifications or compliance training that are approaching expiry, giving them time to act before they become a business risk.

The value isn’t simply that AI has identified something unusual. It’s that the information has been brought together in a way that supports quicker, more informed decisions. The goal isn’t to replace human judgement. The goal is to reduce the effort required to reach an informed decision. That’s a very different proposition from simply adding AI to an existing screen.

Many organisations are already struggling with a familiar challenge. They have more data than ever before, but less time to interpret it. The problem isn’t access to information. The problem is finding the right information at the right moment and understanding what to do with it. This is particularly true across HR, Finance, Procurement and Supply Chain functions where managers are expected to make decisions quickly whilst balancing increasing levels of complexity. The promise of AI has always been to help address that challenge. The reality, however, is that many AI solutions still require significant effort to implement and maintain.

What I find encouraging about the direction Oracle is taking with AI Agent Studio is that it appears focused on practical business outcomes rather than technology for technology’s sake. The emphasis is increasingly on helping organisations build targeted applications that solve real business problems. Not generic AI. Not experimental pilots. Applications designed around specific outcomes.

That might mean reducing the time it takes to onboard a new employee. It might mean helping managers identify retention risks earlier, ensuring expiring certifications are proactively managed, accelerating supplier issue resolution or helping finance teams focus on the transactions that genuinely require attention. The common thread is that the technology becomes a means to an end rather than the end itself.

One of the misconceptions I still hear is that AI Agent Studio is aimed primarily at developers. Having spent time working with the latest experience, I don’t think that’s the case. Technical skills are still valuable. Understanding integrations, security, governance and architecture remains important. But the barriers to entry are reducing significantly. Functional consultants can contribute more directly. Business specialists can participate more actively in the design process. Subject matter experts can help shape solutions using the language of the business rather than the language of software development. I think that’s where the real opportunity lies. The people who understand organisational challenges are often best placed to identify opportunities for AI. Giving those people better tools to express their ideas is just as important as improving the underlying technology itself.

We’re still in the early stages of the agentic journey. Most organisations are exploring use cases, experimenting with ideas and trying to understand where AI can deliver genuine value. There will undoubtedly be lessons learned along the way. There will be successful projects and others that don’t deliver the expected outcomes. But when I look at the direction Oracle is taking with AI Agent Studio, what stands out is the shift from building technology to enabling outcomes. The conversation is gradually moving away from prompts, models and technical configuration. Instead, we’re starting to talk about decisions, actions and business value. For me, that’s the most important change of all.

In the next article in this series, I’ll take a closer look at Oracle’s Builder Assistant and explore what it tells us about the future of application design in Oracle Fusion. After all, if AI can help us describe what we want to achieve rather than how to build it, perhaps we’re closer than ever to closing the gap between business ideas and working solutions.

Please note that all screenshots are the property of Oracle and are used in accordance with Oracle’s Copyright Guidelines.

I’ve written about Oracle’s Agentic Applications a few times now, covering the original announcement, the HCM workspaces and, more recently, my hands-on experience with the Agentic App Builder. Every time I think I’ve reached the end of the story, Oracle introduces something new.

This time, rather than looking at individual features, I want to focus on the bigger picture. What is Oracle actually trying to achieve with Agentic Apps, and why should Fusion customers be paying attention? Because what we’re seeing isn’t just another set of AI features. It’s a shift in how enterprise applications are designed to support work.

For years, ERP and HCM systems have been systems of record. They capture transactions, store data and provide the processes organisations need to run their business. Employees perform tasks and the system records the outcome.

Agentic Applications introduce a different approach. Rather than simply waiting for users to initiate work, the application continuously analyses data, identifies priorities, recommends actions and, where appropriate, helps execute tasks within existing security and governance frameworks.

The result is a move from recording activity to driving outcomes. That’s a significant change.

Employees aren’t removed from the process. In fact, judgement, approval and decision-making remain firmly in human hands. What changes is the amount of manual effort required to gather information, identify next steps and coordinate routine activities.

Oracle describes this as moving from systems of record to systems of outcomes, and the more I see of the strategy, the more that description feels accurate.

One thing that often gets lost in conversations about AI is that not all AI capabilities are the same. Oracle’s approach now spans four distinct layers.

At the most familiar level are generative AI features embedded directly within Fusion user experiences, helping users generate content, summaries and recommendations. The next layer introduces Answer Agents, which provide contextual information and guidance within the flow of work. Beyond that are Workflow Agents that can execute multi-step business processes on behalf of users.

Finally, Oracle has introduced Agentic Applications. These are goal-driven workspaces that coordinate multiple agents and business processes to achieve a specific outcome. The 22 workspaces announced across HCM, ERP, SCM and CX sit firmly within this final category.

For customers starting their AI journey, understanding these layers is important. Not every use case requires a fully agentic application, but they do provide a glimpse of where enterprise software is heading.

Alongside the growing catalogue of pre-built applications, Oracle has continued to invest in the tooling behind them. One of the most important additions is contextual memory, allowing agents to retain relevant information across interactions rather than treating every conversation as a completely new request.

Content Intelligence is another significant development. It enables agents to combine transactional data with enterprise content such as policies, procedures and knowledge articles. This creates opportunities for more sophisticated use cases in areas such as compliance, onboarding and employee support.

Oracle has also expanded support for multimodal interactions, enabling agents to work with information beyond text, including images and voice-based inputs.

For organisations looking at AI as part of a broader technology landscape, Oracle’s support for industry standards such as the Model Context Protocol (MCP) is particularly interesting. It opens the door for Fusion agents to work alongside agents and services running on other platforms, helping organisations avoid creating isolated AI ecosystems.

One challenge every AI programme faces is proving value. Organisations understandably want to move beyond demonstrations and understand whether AI is delivering measurable business benefits. Oracle’s new Agent ROI Dashboard aims to address this. The dashboard tracks metrics such as usage, successful completions, time savings and cost savings, giving organisations a way to monitor the impact of individual agents over time.

While the methodology behind any ROI calculation should always be considered carefully, the availability of these metrics provides a practical starting point for conversations with project sponsors and steering committees. Instead of discussing potential benefits in theory, organisations can begin measuring outcomes based on actual usage patterns.

The HCM workspaces have received a lot of attention since their launch, but some of the ERP and SCM use cases are equally compelling.

One example is the Design-to-Source Workspace. Traditionally, moving from product design to sourcing can involve multiple teams, disconnected systems and significant manual effort. The workspace connects these activities, helping organisations move more efficiently from bill of materials creation through to supplier engagement and sourcing decisions. What’s particularly interesting is that the workspace doesn’t simply display information. It continuously monitors progress, identifies risks and highlights areas requiring attention.

Another example is the Collectors Workspace within ERP. Collections teams often spend considerable time gathering information from different sources before they can determine the appropriate next action. The Collectors Workspace brings together customer history, disputes, payment behaviour and other relevant information into a single view, helping teams focus their attention where it will have the greatest impact.

In both examples, the goal isn’t to replace experienced employees. It’s to remove the effort associated with collecting and organising information, allowing people to spend more time making informed decisions.

Oracle has continued to expand its HCM agentic application portfolio, with workspaces covering areas such as hiring, career development, workforce operations, employee support and team management. Despite the variety of use cases, a common theme runs throughout them all.

The applications take responsibility for gathering information, coordinating activities and surfacing recommendations, while people remain responsible for decisions that require judgement, experience and empathy. That balance is important because successful AI adoption isn’t about removing human involvement. It’s about enabling people to focus on higher-value work.

The most interesting aspect of Oracle’s Agentic Applications isn’t any individual workspace or feature. It’s the direction of travel. For years, organisations have invested heavily in putting consistent processes and accurate data into their enterprise systems. Agentic Applications represent Oracle’s next step, using that foundation to actively help organisations achieve business outcomes rather than simply record activity. Not every organisation will adopt these capabilities at the same pace, and governance, security and change management will remain critical considerations.

However, the conversation is already changing. Instead of asking how technology can automate individual tasks, organisations can start asking how it can help deliver broader business objectives. That is a much bigger shift than simply adding another AI feature to an application.

If you’re exploring Oracle’s AI strategy, my recommendation is to start small. Identify a business process where information gathering and routine coordination consume significant time, evaluate one of the pre-built agentic applications, and measure the results. The new Agent ROI Dashboard gives organisations a practical way to begin that journey using real data rather than assumptions.

Please note all screenshots are the property of Oracle and are used according to their Copyright Guidelines