Oracle Fusion Agentic Apps: From System of Record to System of Outcomes

~ K8 Mead ~ Leave a comment

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

Building Workflow Agents in Oracle AI Agent Studio: From Configuration to Production

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It’s one thing to understand Workflow Agents in theory. It’s another to sit down in Oracle AI Agent Studio and start building one. In my previous blog, I explored the architecture behind Workflow Agents and how Oracle has designed them to combine AI reasoning with structured business processes.

This time, I wanted to focus on the practical side. What do you actually see when you start building a Workflow Agent? Which settings matter? How do triggers work? What’s the best way to handle errors? And which data nodes should you use for different scenarios? These are the questions that tend to come up once organisations move beyond the concept stage and start planning real-world implementations.

Creating a new Workflow Agent is straightforward, but there are a few important decisions to make from the outset. The Details tab is where you define the basics, including the agent name, code, application family and product area. While these may seem like simple administrative fields, they’re worth getting right because they determine how the agent is categorised within AI Agent Studio and how it appears within monitoring and reporting.

The LLM tab is where you select the large language model that will power the agent. Workflow Agents use GPT-5 mini by default, which is more than capable of handling many common automation scenarios such as document extraction, classification and policy-based decision making.

One point worth noting is that not all models are licensed in the same way. The OSS LLM is free to use and is provided as part of your Oracle subscription, while all other LLMs are deemed premium and therefore are charged as AI Units are consumed. For more information, please look at my previous blog on AI Agent pricing structures.

The Chat Experience tab allows you to enable file uploads. This becomes particularly useful when your workflow needs to process documents such as supplier quotes, invoices or legal correspondence. Files uploaded at runtime can then be passed directly to a Document Processor node for extraction and analysis.

One of the first questions people ask is how a Workflow Agent actually starts running. Oracle currently supports three trigger types, each designed for different use cases.

Firstly, webhook triggers. These offer the greatest flexibility. When configuring a webhook, you define one or more input variables which become available through a REST endpoint. External systems can then pass information into the workflow when triggering the agent. This approach works particularly well when integrating with Oracle Fusion processes, third-party applications or custom solutions that need to launch an automated workflow in response to a specific business event.

Email triggers are where Workflow Agents start to open up some interesting automation opportunities. After configuring an inbound Microsoft or Google email account within AI Agent Studio, every email received by that mailbox can automatically initiate a workflow. The agent has access to both the email content and any attachments, allowing it to classify, extract and process information without manual intervention.

For example, a payroll team could automatically process court garnishment orders received via email, while an accounts payable team could analyse supplier invoices arriving in a dedicated mailbox. The key benefit is that users don’t need to actively launch the process. The workflow begins as soon as the email arrives.

Schedule triggers are designed for recurring tasks. Whether you need a workflow to run every 30 minutes, every Monday morning, or on a specific schedule throughout the month, Oracle provides flexible scheduling options to support it. This makes them ideal for monitoring activities, batch processing and routine validation tasks that need to run automatically in the background.

If there’s one area that’s often overlooked during development, it’s error handling. Most people focus on getting the workflow working and only think about failures later. Unfortunately, it’s usually the first thing they need when moving into production.

Oracle provides a dedicated Error Handling tab where you can configure automated notifications whenever a workflow encounters an unrecoverable issue. Rather than sending a generic failure message, you can include contextual information such as:

  • The workflow name
  • Trace ID
  • The node that failed
  • The error message
  • Business data being processed at the time

This makes troubleshooting far quicker because support teams receive meaningful information rather than simply being told that something went wrong.

It’s also worth remembering that individual nodes can have their own error-handling paths. Rather than terminating the workflow entirely, you can redirect failures to alternative branches that perform logging, update status values or notify users before ending gracefully. In practice, combining workflow-level notifications with node-level recovery paths creates a much more resilient solution.

One of the more confusing aspects of Workflow Agent development is knowing which data node to use. Although several of the available nodes appear similar at first glance, they serve very different purposes.

The Document Processor is the starting point for document-based workflows. It extracts content from files such as PDFs, Word documents, HTML pages and scanned images, making the information available for downstream processing.

Vector Write creates embeddings and stores them within a vector index. Think of this as the step where knowledge is prepared and stored so that it can be searched later.

Vector Read retrieves relevant content from an existing vector index. This is typically used when a workflow needs to locate policies, procedures or reference material to support a decision.

The RAG Document Tool combines retrieval and grounding into a single step. Instead of manually connecting retrieval and generation components, the node retrieves relevant content and uses it to produce a grounded response from the LLM.

For knowledge-based workflows, the most common pattern is: Document Processor → Vector Write → Vector Read → LLM. Understanding this flow early can save a considerable amount of redesign later.

Perhaps the most important feature within Workflow Agents is the Human Approval node. This allows the workflow to pause and request approval before carrying out a specific action. The workflow only continues once an authorised individual has reviewed and approved the decision.

This approach strikes a balance between automation and governance. Routine tasks can be handled automatically, while higher-risk scenarios remain subject to human oversight. This is particularly important in HR, payroll and finance processes, where an incorrect decision can have a direct impact on employees or suppliers.

In many ways, this ability to blend AI-driven automation with structured controls is what makes Workflow Agents different from many other agent frameworks.

One of the most significant developments to AI Agent Studio is METRO (Measurement, Evaluation and Testing for Real-time Observability). METRO provides visibility into how agents are performing through dashboards covering:

  • Accuracy
  • Latency
  • Token usage
  • Execution tracing
  • Evaluation scoring

For Workflow Agents, detailed tracing is particularly valuable because it allows teams to see exactly how an instance progressed through the workflow, identify bottlenecks and pinpoint failures.

Oracle have also introduced AI governance capabilities, including guardrails on requests and responses, instruction protection and integration with Oracle’s AI Governance framework. For organisations looking to deploy Workflow Agents into production environments, these additions provide greater confidence around monitoring, governance and ongoing optimisation.

Ready to Start Building? The best advice I can give is to start small. Choose a process you understand well. Something with clear inputs, defined outcomes and a manageable number of exceptions.

Build a simple proof of concept first. Get the trigger working. Add a data node. Introduce an LLM step. Test the error handling. Then gradually expand the workflow as your confidence grows.

Oracle has made Workflow Agents surprisingly accessible, and the observability features make it much easier to understand what’s happening behind the scenes as you develop and refine your solution. If you’re considering Workflow Agents and want to understand where they could deliver value in your organisation, now is a good time to start exploring the possibilities.

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

Oracle’s AI-Powered Future for Recruiting: Connecting the Entire Hiring Journey

~ K8 Mead ~ 2 Comments

Recruitment teams are under pressure from every angle. Candidates expect faster responses, personalised interactions and greater transparency throughout the hiring process. At the same time, organisations are increasingly shifting towards skills-based hiring and talent management, often while juggling disconnected systems, inconsistent processes and growing workloads. This is where Oracle’s latest AI-powered recruiting capabilities are aiming to make a real difference. Rather than introducing another standalone AI tool, Oracle is embedding intelligent agents throughout the recruitment lifecycle to help organisations attract, engage, hire and onboard talent more effectively.

Oracle’s AI Agent Framework for Recruiting brings together a collection of specialised AI agents that support different stages of the hiring process. Instead of acting as isolated tools, these agents work together across the recruitment journey, sharing context and information to create a more seamless experience for both candidates and hiring teams. The objective is straightforward: improve candidate experiences, reduce manual effort and help organisations make better hiring decisions more quickly.

One of the most visible innovations is Career Coach, Oracle’s candidate-facing AI assistant. Integrated directly into career sites, Career Coach helps candidates discover relevant opportunities based on their skills, experience and career interests. Rather than searching through dozens of vacancies, candidates receive personalised recommendations and guidance throughout the application process. They can upload CVs, ask questions and complete applications through a conversational experience that feels far more natural than traditional online forms.

Perhaps most interesting is Career Coach’s ability to provide personalised application feedback. Candidates can receive recommendations on how well their application aligns with the role requirements, highlighting skills, qualifications or experiences that may strengthen their submission. For organisations, this has the potential to improve application quality while creating a more supportive and engaging candidate experience.

Maintaining regular communication throughout recruitment remains a challenge for many organisations, particularly when managing high application volumes. Oracle addresses this through AI-powered messaging capabilities that can answer routine candidate questions and provide updates through channels such as email, SMS and WhatsApp. Candidates can receive information about interview stages, scheduling, timelines and next steps without waiting for a recruiter to respond. The result is a more responsive recruitment process while allowing recruiters to focus their attention on higher-value interactions.

Recruiters are not the only beneficiaries of Oracle’s AI investment. Hiring managers are often required to review large volumes of candidate information, interview feedback and assessment results before making decisions. Oracle’s Hiring Assistant helps simplify this process by generating concise candidate summaries and surfacing key information from across the recruitment lifecycle.

Instead of reviewing multiple documents and feedback forms, hiring managers can quickly understand a candidate’s suitability, relevant experience and alignment with role requirements, helping them make more informed decisions while reducing administrative effort.

Interviews remain one of the most important stages of the recruitment process, yet they can be time-consuming to organise and document. Oracle is introducing new capabilities that help streamline interview management. Interview Companion, arriving across upcoming releases, will assist interviewers before, during and after interviews.

Before the interview, it can generate interview guides tailored to the specific role and candidate. During the interview, it can help interviewers track discussion topics and ensure key areas are covered. Afterwards, it can automatically generate transcripts, summaries, interview notes and suggested follow-up actions.

These capabilities have the potential to improve consistency across interviews while reducing the administrative burden placed on hiring managers and interviewers. Oracle is also expanding its partner ecosystem through integrations with organisations such as Eightfold.ai and Phenom, bringing additional capabilities including AI-driven interviewing and skills assessment.

For organisations operating in sectors such as retail, hospitality and frontline services, recruitment often centres around hiring large numbers of employees quickly. Oracle’s Hiring Workspace for Store Managers has been designed specifically for these environments. The workspace provides managers with a simple, action-focused view of recruitment activity, highlighting vacancies requiring attention, identifying potential pipeline issues and recommending next steps.

Managers can quickly identify strong candidates, progress applications and manage recruitment activity without needing extensive experience with a traditional applicant tracking system. For organisations managing high-volume recruitment, this could significantly improve hiring speed and reduce delays in filling critical roles. It should be noted that this is an Agentic App and as such requires the Agentic Application Platform licence.

Finding talent remains one of the most challenging aspects of recruitment. Oracle is investing heavily in AI-powered sourcing capabilities to make this process more efficient. Recruiters can use natural language search to identify candidates based on skills, experience and other criteria, without relying on complex search strings. AI-powered recommendations help identify suitable candidates from existing talent pools, reducing the risk of overlooking qualified individuals who may already exist within the organisation’s database. Combined with automated outreach and personalised engagement, these capabilities aim to help recruiters build stronger pipelines while reducing manual sourcing effort.

The recruitment experience does not end when an offer is accepted. Oracle is continuing to strengthen the transition between recruitment and onboarding by helping organisations maintain communication with new hires from offer acceptance through to their first day. Automated communications, onboarding updates and personalised engagement help reduce the risk of candidate drop-off while creating a more consistent experience. This continuity can be particularly valuable in competitive talent markets, where maintaining engagement after offer acceptance is just as important as attracting candidates in the first place.

Oracle’s vision for recruiting is becoming increasingly clear. Rather than viewing AI as a collection of disconnected features, Oracle is building an ecosystem of specialised agents that work together across the entire hiring lifecycle. Many of these capabilities are already available today, while others will be introduced through upcoming releases such as 26C and 26D. Together, they represent a significant shift towards more intelligent, proactive and personalised recruitment experiences.

For organisations looking to improve candidate engagement, increase recruiter productivity and support skills-based hiring strategies, these innovations offer a glimpse into how recruitment technology is evolving from process automation to genuinely intelligent talent acquisition.

Please note that some capabilities discussed may require additional Oracle licensing, including Oracle Recruiting Booster. Organisations should confirm availability and licensing requirements with Oracle before implementation.

All screenshots are the property of Oracle and are used in accordance with Oracle’s Copyright Guidelines.

Oracle Fusion HR Help Desk: What AI Actually Looks Like in Practice

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When Oracle talks about AI in Fusion Applications, it’s easy to assume we’re talking about a chatbot sitting on top of an existing process. In reality, Oracle has been quietly embedding AI throughout the entire HR Help Desk lifecycle, from the moment an employee has a question through to the point a case is resolved and closed.

What’s particularly interesting is that these capabilities aren’t isolated features. They work together to create a service experience that is increasingly proactive, intelligent and efficient for both employees and service teams.

Let’s walk through what that journey looks like in practice. The best help desk case is often the one that never needs to be raised. Oracle’s AI-powered employee assistance capabilities, available through the My Help experience, allow employees to ask questions in natural language and receive immediate guidance based on knowledge articles and HR process information. Instead of navigating FAQs or searching through documentation, employees can simply ask questions in their own words and receive conversational responses. Whether an employee wants to understand a policy, find information about benefits, or work out how to complete a process such as requesting leave, the AI assistant acts as the first point of contact and attempts to resolve the issue through self-service.

This is also the direction Oracle is taking strategically. Organisations still using Oracle Digital Assistant (ODA) skills for HR Help Desk should be aware that Oracle is moving towards the newer Redwood-based AI Agent framework, with legacy ODA capabilities being phased out in favour of the new experience.

Of course, self-service won’t solve every issue. When additional support is required, Oracle’s Help Desk Request Creator AI Agent can automatically create a help desk request using the details already captured during the conversation. Information such as the request description, category and other key details can be populated automatically, avoiding the frustrating experience of employees having to repeat everything they’ve already explained. For employees, that means a smoother support experience. For service teams, it means better-quality requests arriving in the queue with the right information from the outset.

One of the most time-consuming parts of any service desk operation is getting requests to the right team. In many organisations, someone still needs to review incoming requests, determine the nature of the issue and assign it appropriately. Oracle uses machine learning to automate much of this process. When a help desk request is created, whether through self-service, email or another channel, Oracle can analyse the content and predict the most appropriate category before routing it to the correct queue.

Imagine an employee emails HR because their tax code is incorrect on their payslip. Rather than waiting for manual triage, the system can identify the request as payroll-related and route it directly to the relevant team. What makes this particularly valuable is that the model improves over time. Oracle trains the categorisation engine using historical case data, allowing it to learn from previously resolved requests and continually improve its accuracy. Administrators can monitor performance through dedicated insights dashboards, helping them understand how effectively the model is classifying requests and what impact it is having on resolution times.

Once a case reaches a service agent, Oracle introduces another layer of AI support. A common challenge within HR Help Desk is the amount of time agents spend reviewing case history before taking action. Long-running cases often contain multiple updates, conversations and handovers, making it difficult to quickly understand what’s happened so far.

Oracle addresses this through a series of AI-powered summarisation capabilities. Agents can generate instant summaries of requests, review concise overviews of recent activity and use AI-generated handover notes when transferring cases between teams. When the case is resolved, AI can also generate draft resolution notes to help document the outcome.

These may seem like small improvements individually, but collectively they remove a significant amount of administrative effort from the case management process. In 26B, Oracle enhanced these capabilities by rebuilding them as configurable AI Agents managed through AI Agent Studio. While the experience remains familiar to end users, organisations gain much greater flexibility over how these capabilities operate.

Beyond summarisation, Oracle also provides a Resolution AI Agent that can analyse an open case and suggest a potential response. Rather than manually searching for relevant knowledge articles or previous resolutions, agents receive a recommended draft response based on the details of the current case. Importantly, the AI does not make decisions on behalf of the service agent. It provides recommendations, while the agent remains responsible for reviewing and approving the final response.

Of all the AI capabilities currently available within HR Help Desk, Case Analyzer is arguably the most transformative. Complex cases often involve multiple stakeholders, tasks, updates and handovers. Understanding the history of a case can sometimes take longer than resolving the issue itself. Case Analyzer is designed to address that challenge.

When launched, the AI Agent reviews the entire case record, including notes, messages, tasks, action plans and other relevant information. It then presents that information through a conversational interface that allows agents to explore the case in far greater detail. Agents can view a timeline of events, identify important developments, ask specific questions about the case and receive suggested next-best actions. The tool can also highlight potential escalation risks and help agents understand whether additional attention may be required.

What I particularly like about this capability is that it moves beyond simple summarisation. Rather than giving you a static overview, it allows you to have an interactive conversation with the case itself. For newer service agents, this can significantly reduce the time needed to understand a complex issue. For experienced agents, it provides confidence that important details haven’t been overlooked. Oracle has continued to invest in this capability, with 26B introducing configurable starter prompts that help guide users towards the types of questions they can ask.

A controlled availability feature that is also worth watching is the evolution of My Help into a broader employee workspace. Rather than focusing solely on conversational support, the workspace brings together employee tasks, open requests and relevant knowledge content into a single AI-driven experience. While still in the early stages of adoption, it provides a useful glimpse into how Oracle sees the future of employee support. It should be noted that this utilises the Agentic Application Platform which requires a separate licence.

The real value is not any single AI feature. It’s the way they work together. Employees receive assistance before a case is raised. Requests are categorised and routed automatically. Agents receive summaries, recommendations and drafted responses. Complex cases can be analysed through a dedicated AI assistant. Resolution notes can be generated automatically at closure. Taken together, these capabilities represent a shift away from traditional ticket management and towards a more intelligent service model that actively supports both employees and HR service teams throughout the lifecycle of a request.

There is another important point worth mentioning. All of these capabilities are embedded within Oracle Fusion Applications and operate within the existing security and access framework. For organisations handling sensitive HR data, that matters just as much as the AI functionality itself.

If you’re already using Oracle HR Help Desk, it’s worth taking a fresh look at what’s available in your environment. Many of these capabilities have arrived gradually over recent releases, making them easy to overlook. Taken together, however, they represent one of the most comprehensive examples of how Oracle is embedding AI directly into everyday business processes.

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

Choosing the Right LLM for Oracle AI Agent Studio: Balancing Cost, Performance and AI Unit Consumption

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When Oracle introduced AI Agent Studio, it gave organisations the ability to build and deploy custom AI agents directly within Oracle Fusion. As customers move from experimentation into production, one question comes up repeatedly: So, which LLM should I use? At first glance, this looks like a technical decision. In reality, it has significant implications for cost, performance and the way your AI agents operate at scale. With Oracle’s new AI Unit pricing model, the LLM you choose can directly affect how quickly you consume your AI allocation, making model selection an important part of both solution design and budget planning.

The Good News is that Oracle Gives You Choice, Oracle AI Agent Studio allows organisations to select the LLM that best fits their requirements. For some use cases, a basic model may provide everything you need. For others, a premium model delivers the additional reasoning and conversational capabilities required for more complex business processes.

The important thing is that Oracle is not forcing customers into a single approach. Instead, organisations can align model selection to the specific needs of each AI agent. This flexibility is particularly valuable because not every business process requires the same level of intelligence.

I get asked a lot, how can I check which LLM my agent is using. If you go to AI Agent Studio, find your agent and click on the edit button and then the cog button, the below options will appear. Click on the LLM tab and review what is selected. In this example, the default was GPT-4.1 Mini which is a premium LLM, so I’ve switched it to the free GPT OSS. Just click update and it is all saved.

One of the biggest misconceptions surrounding AI agents is that every use case requires the most powerful model available. In practice, many agents perform relatively focused tasks:

  • Classifying documents
  • Extracting information from forms
  • Routing requests
  • Generating standard responses
  • Performing workflow actions

For these kinds of activities, a basic LLM may deliver perfectly acceptable results while keeping AI Unit consumption lower. The challenge is that many organisations instinctively select a premium model for every use case without fully understanding the cost implications. As with any cloud service, right-sizing matters.

Another key consideration is the impact on AI Unit consumption. Oracle’s new pricing model is based on AI Units rather than traditional licensing metrics such as users, agents or tokens. Every Fusion customer receives a monthly allocation of AI Units, with additional capacity available if required.

Where this becomes relevant is the relationship between the selected LLM and AI Unit consumption. During Oracle’s recent pricing briefing, examples showed that general actions using a premium model consume AI Units, while actions using a basic LLM can often be performed without additional AI Unit charges. This means the model choice behind an agent can have a direct effect on ongoing operational costs. For organisations planning multiple custom agents, this quickly becomes an architectural consideration rather than simply a design preference.

When deciding whether to use a basic or premium model, I encourage customers to focus on business risk and business value. Ask yourself:

  • What is the impact if the answer is wrong?
  • Does the process involve customer interactions?
  • Is there financial or compliance risk?
  • Does the agent need to reason across multiple pieces of information?
  • Is a human reviewing the output before action is taken?

For low-risk activities, a basic model may be entirely appropriate. For more complex scenarios, such as HR advisory services, financial decision support or customer-facing interactions, the additional capability of a premium model may justify the additional AI Unit consumption. The goal should not be to minimise AI Unit usage at all costs. The goal should be to use the right model for the right task.

One particularly important point that emerged from Oracle’s pricing session is that AI Units consumed in non-production environments also count towards usage when premium models are being used. This means development, testing and experimentation activities can contribute to overall AI consumption.

Many organisations are accustomed to thinking about production workloads when budgeting cloud services, but AI introduces a different dynamic. Agent testing, prompt refinement and user acceptance testing all have the potential to generate usage. As AI adoption grows, development and testing practices will need to evolve to take this into account.

The reality is that most organisations do not yet have mature governance processes for AI consumption. That will need to change. Oracle has already introduced observability and analytics capabilities to help customers understand performance, accuracy and AI Unit consumption. Future releases will also provide additional controls such as budgets and consumption caps. These capabilities will become increasingly important as organisations deploy larger numbers of AI agents across HR, Finance, Procurement, Supply Chain and Customer Experience. Without visibility, it becomes difficult to understand whether the business value being generated justifies the cost being incurred.

When building agents in Oracle AI Agent Studio, my recommendation would be to always start with the business outcome rather than the technology. Not every use case requires a premium model, and not every process should be optimised purely for cost. The most successful organisations will take a balanced approach, matching model capability to business need. Use premium models where reasoning, accuracy and user experience are critical. Use simpler models for straightforward, repetitive tasks where the additional intelligence offers limited value.

Most importantly, monitor consumption from the beginning. AI Units are becoming the new currency of Oracle AI, and understanding how your model choices affect usage will be essential to scaling AI successfully across the enterprise.

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

Turning on AI in Fusion Procurement

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While many organisations are still working out where AI can genuinely improve business processes, Oracle has been steadily embedding generative AI into Oracle Fusion Cloud Procurement. Some of these capabilities are already well established. Both the Procurement Policy Advisor and Supplier Portal Advisor have been available for around a year and are being used by many organisations to improve self-service, increase process efficiency and reduce support overhead. More recently, Oracle has introduced agentic applications such as the new Sourcing Command Centre, released in Oracle Fusion Cloud Procurement 26B, which moves beyond answering questions to actively identifying issues and recommending actions.

The result is not simply faster access to information. These capabilities are designed to reduce administrative effort, improve compliance, support better decision-making and help procurement teams focus on higher-value activities. Three innovations illustrate how Oracle is approaching AI in procurement today: Procurement Policy Advisor, Supplier Portal Advisor and Sourcing Command Centre.

One of the biggest frustrations for employees raising requisitions is finding the information they need. Questions about purchasing policies, approved suppliers, spending limits or equipment refresh cycles often result in searches through intranet pages, policy documents or calls to the procurement team. Oracle’s Procurement Policy Advisor addresses this challenge by providing answers directly within the Self Service Procurement experience. Users can ask questions in plain English and receive immediate responses based on their organisation’s procurement policies and supporting documentation. The capability is designed to provide seamless access to procurement policies while employees are ordering the products and services they need.

Whether an employee wants to know when they are eligible for a replacement laptop or which products can be ordered through approved catalogues, the information is available without leaving the requisition process. Importantly, responses are grounded in approved source documents, allowing users to see exactly where the information has come from. This helps build confidence in the accuracy of the advice while supporting consistent policy compliance across the organisation.

For procurement teams, the benefits are equally significant. By reducing repetitive policy queries and making guidance available at the point of need, organisations can improve the user experience while reducing demand on internal support teams. Oracle specifically highlights streamlined access to policy documents and improved compliance as key outcomes of the solution. As one of Oracle’s earlier procurement AI capabilities, Procurement Policy Advisor is already a proven and mature use case, giving organisations a practical way to deliver the benefits of AI without changing core procurement processes.

Suppliers often need support with routine activities such as invoice submission, payment enquiries or updating organisation details. While these requests may seem straightforward, they can consume significant amounts of time for procurement and accounts payable teams.

Supplier Portal Advisor extends Oracle’s AI capabilities to the supplier community by providing a self-service assistant within the Supplier Portal. Oracle describes it as a chat-based experience that answers policy, process and how-to questions directly within the application using an organisation’s own supplier support content. Rather than searching through documentation or contacting support teams, suppliers can receive immediate guidance within the portal. Organisations can tailor the advisor using their own supplier-facing documentation, policies, invoicing instructions and onboarding guidance, ensuring answers reflect their specific ways of working. This creates a more consistent experience for suppliers while reducing the number of routine enquiries that procurement and finance teams need to manage.

The value becomes particularly apparent for organisations with large supplier populations, where even a small reduction in support requests can deliver meaningful efficiency gains. Suppliers are able to resolve questions themselves, while procurement teams can focus their time on supplier relationships, sourcing activities and strategic initiatives rather than handling repetitive enquiries.

Like Procurement Policy Advisor, Supplier Portal Advisor is no longer a new concept. It has been available since 2025 and is already helping organisations extend AI-driven self-service beyond employees and into their wider supplier ecosystem.

While answering questions is valuable, Oracle’s Sourcing Command Centre takes AI a significant step further by helping sourcing teams identify risks, prioritise activities and take action more quickly. Unlike the Policy Advisor and Supplier Portal Advisor, which are now established capabilities, Sourcing Command Centre is a new addition introduced as part of Oracle Fusion Cloud Procurement 26B. Oracle positions it as an AI-powered agentic command centre where specialist sourcing agents continuously analyse negotiations, supplier participation and award readiness to identify risks, opportunities and recommended actions.

Organisations interested in adopting Sourcing Command Centre should also be aware that it sits within Oracle’s broader Agentic Applications strategy. Customers will need to license the Agentic Applications Platform before they can take advantage of the capability, making it important to understand both the business case and licensing implications as part of any evaluation.

Managing multiple sourcing events often requires buyers to monitor negotiations, track supplier participation, review timelines and assess award options across numerous screens and reports. Important issues can be overlooked simply because of the volume of information involved. Sourcing Command Centre brings these activities together into a single workspace. AI-generated summaries highlight negotiations that require attention and prioritise actions based on sourcing data and business rules. Oracle states that the solution surfaces negotiations needing immediate intervention, including events with low supplier participation, negotiations approaching close dates and sourcing activities awaiting award decisions.

Rather than simply identifying issues, the solution enables users to act directly from the workspace. Recommended actions can include extending negotiations, updating schedules, resuming paused events and communicating with suppliers without navigating elsewhere in the application. The Command Centre can also assist with award decisions by analysing supplier responses and recommending award scenarios. This enables sourcing teams to evaluate options more efficiently while maintaining visibility of how recommendations have been generated. Oracle highlights AI-recommended award actions, negotiation-specific analysis and the ability to apply recommended awards directly from the workspace.

For organisations looking to increase procurement productivity and accelerate sourcing cycles, this represents a significant shift. Rather than AI acting solely as an information assistant, it becomes an active participant in managing procurement processes and helping users drive outcomes.

Although these capabilities address different aspects of the procurement lifecycle, they share a common theme. The Procurement Policy Advisor and Supplier Portal Advisor focus on providing accurate, contextual answers based on an organisation’s own policies and documentation. Both have now matured into established capabilities that are delivering value in real-world implementations. Sourcing Command Centre builds on that foundation by helping procurement professionals identify priorities and take action. It represents Oracle’s next step towards agentic applications that can not only answer questions but also recommend and execute tasks within defined business controls.

Together, these innovations demonstrate Oracle’s practical approach to AI in Fusion Applications. Rather than replacing procurement professionals, they are designed to remove friction, surface relevant information at the right time and support more informed decision-making. For organisations using Oracle Fusion Procurement, AI is no longer an experimental technology. It is becoming an increasingly practical part of day-to-day procurement operations, helping employees, suppliers and sourcing teams work more efficiently while maintaining visibility, governance and control.

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AI for Managing Cash in Oracle Fusion ERP

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Cash management has traditionally relied on a combination of spreadsheets, separate banking platforms and a significant amount of manual effort to build forecasts, manage liquidity and chase overdue payments. Oracle is taking a different approach by embedding AI directly into Oracle Fusion Cloud ERP, helping finance teams make better decisions using the same platform where transactions already exist.

Recent developments across cash forecasting, banking connectivity, payments and collections all point towards a common goal: helping finance and treasury teams spend less time gathering information and more time acting on it. Rather than introducing another standalone tool, Oracle is bringing intelligence directly into day-to-day finance processes.

For many organisations, obtaining an accurate view of future cash position remains a challenge. Forecasting often relies on multiple spreadsheets, assumptions and manual updates, making it difficult to respond quickly when circumstances change.

Oracle’s Predictive Cash Forecasting capability aims to improve visibility by bringing together current cash balances, expected inflows, expected outflows and cash flow projections into a single rolling forecast. Finance and treasury teams can view projected cash positions over future periods, supported by visual cash flow analysis and cash position forecasting.

One particularly useful feature is the ability to compare forecasted and actual cash flows across successive periods. This helps identify variances early, allowing treasury teams to investigate unusual spending patterns or unexpected changes in cash movement before they become larger issues.

The AI capability becomes more apparent through Oracle’s forecasting methods. Organisations can review forecasts generated using different approaches, including machine learning models, statistical forecasting techniques, moving averages and trend-based methods. Rather than relying on a single forecasting model for every scenario, finance teams can select the most appropriate forecasting approach for different periods within the forecast horizon.

This flexibility can help improve forecast accuracy while giving users greater confidence in the forecasts they rely on for decision-making.

Before planning an implementation, it is worth noting that Predictive Cash Forecasting requires Oracle EPM Planning licensing. Organisations should confirm their current licensing position and security roles before including it within their roadmap. Businesses already using the standalone Predictive Cash Forecasting capability within Oracle EPM can continue to do so, as the existing solution remains fully supported.

Banking connectivity has often been an area where finance teams depend on custom integrations, file transfers and manual reconciliation activities. Oracle’s embedded banking strategy seeks to simplify these processes by connecting Oracle Fusion Cloud ERP more directly with participating banking partners.

The capability supports a range of services including virtual card payments, direct banking connectivity, supply chain finance, bank account validation and real-time banking information. Oracle continues to expand its ecosystem of banking partnerships to support these services.

From an operational perspective, embedded banking simplifies several key activities, including bank account setup, receipt processing, bank statement processing, payment execution and balance visibility. Standard support for ISO 20022 payment formats also reduces the effort traditionally associated with formatting payments for different banking providers.

One feature attracting particular attention is supplier bank account validation. During supplier onboarding, bank account details can be validated automatically, helping organisations confirm account ownership and reduce payment risks before transactions are processed. The immediate benefits are clear. Finance teams can reduce failed payments, improve supplier data quality and strengthen controls designed to prevent payment fraud.

Organisations should be aware that bank account validation currently has geographical and banking partner limitations. If this capability forms part of your business case, it is worth discussing current coverage with Oracle before implementation to ensure it aligns with your supplier population and operating regions.

Within Accounts Payable, Oracle’s Payments Agent focuses on helping organisations make better payment decisions while improving processing efficiency. For many finance teams, payment runs are often viewed as an administrative activity. However, payment timing can have a significant impact on working capital, supplier relationships and available discounts.

Oracle’s Payments work area provides visibility of payment proposals, supplier offers and outstanding balances within a single workspace. This makes it easier for AP teams to identify early payment discounts, supplier incentives and rebate opportunities that may otherwise be overlooked.

Additional views allow users to analyse payment information at supplier level, providing insight into balances, payment terms and instalment arrangements. This helps finance teams validate payment recommendations and understand the potential impact of alternative payment decisions. The result is a more informed approach to payment management, where teams can balance cash preservation with supplier engagement and commercial opportunities.

While all of these developments are valuable, the most significant day-to-day impact may come from Oracle’s AI-driven Collections Workspace. Managing collections has traditionally been heavily dependent on individual collector experience. Determining who to contact, which accounts present the highest risk and how best to approach each customer can consume a significant amount of time.

Oracle’s Collections Workspace brings this information together in a prioritised view, helping teams focus on the accounts that require immediate attention. Customers can be ranked based on overdue balances, risk factors, broken payment commitments and unresolved disputes.

The embedded AI assistant provides collectors with account summaries, collection histories and recommended next actions. Rather than spending time researching account details before every customer interaction, collectors can access the information they need in a single workspace.

Perhaps most impressive is the ability to generate suggested call preparation notes based on a customer’s payment history and current account status. This helps collectors enter conversations better prepared and with a clearer understanding of the issues that need to be resolved.

The workspace also supports post-call follow-up activities. By analysing conversation records and account information, Oracle can identify actions that may require attention and help route information to the appropriate teams.

For collections teams, the practical benefit is simple: less time spent preparing for conversations and more time focused on resolving outstanding debt and improving cash collection performance. It is important to note that the Collections Workspace forms part of Oracle’s Agentic Applications strategy and requires the appropriate licence to be enabled.

As with any new capability, success depends on understanding the prerequisites before embarking on an implementation. Predictive Cash Forecasting requires Oracle EPM Planning licensing, whether accessed through Oracle ERP or a standalone EPM environment. Appropriate security roles should also be reviewed early in the project lifecycle. Organisations considering bank account validation should confirm current banking partner support and geographical coverage before building business processes around the capability.

The good news is that these innovations do not require a complete finance transformation programme to begin delivering value. Many organisations can adopt individual capabilities incrementally, allowing them to target specific business challenges while building towards a wider AI-enabled finance strategy.

What stands out across all of these developments is that Oracle is focusing on practical outcomes rather than AI for its own sake. Whether it is improving cash forecasting accuracy, reducing payment risk, identifying supplier discount opportunities or helping collections teams recover debt more effectively, the emphasis is on solving real business problems.

For organisations looking to improve liquidity management, strengthen financial controls and increase efficiency across finance operations, these capabilities are well worth exploring.

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Oracle Fusion’s New Data Extraction Tool: Getting Your Data Out, Faster and Easier Than Ever

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Oracle has introduced a significant enhancement to data extraction in Fusion Cloud Applications with the Read-Optimised Data Store (RODS) and the new Data Extraction Tool. Together, these capabilities provide a modern approach to accessing Fusion data for reporting, analytics and integration workloads, without placing additional demand on live transactional systems.

For organisations looking to feed data warehouses, support operational reporting or enable near real-time integrations, this represents an important shift in how Fusion data can be accessed and consumed.

Traditionally, organisations have relied on a combination of BICC (Business Intelligence Cloud Connector), REST APIs and BI Publisher (BIP) reports to extract data from Fusion. Each approach has advantages, but also limitations. BICC provides reliable bulk extracts, although these are batch-based by design. REST APIs can deliver more current information but often require significant orchestration and development effort. BI Publisher allows flexible data extraction, but queries execute against the same database that supports day-to-day business transactions.

RODS addresses these challenges by separating data extraction from transactional processing. Rather than querying the live application database, extracts run against a dedicated read-optimised environment that is continuously synchronised with Fusion Cloud. This allows large-scale data extraction activities to take place without competing for resources with business operations.

RODS, or Read-Optimised Data Store, is built on Oracle Autonomous AI Lakehouse technology and is designed specifically for high-volume, read-intensive workloads. Fusion data is continuously replicated into RODS using Oracle GoldenGate technology, providing a near real-time representation of transactional data. The result is a platform that supports reporting, analytics and extraction activities without impacting application performance.

Importantly, Oracle is deploying RODS to customers through its Oracle Application Platform technology upgrade programme. There is no separate infrastructure to purchase or manage, and customers can request early enablement through Oracle Support where available.

The Data Extraction Tool provides a modern, Redwood-based interface for creating and managing data extracts.

Available from release 26A onwards, the tool enables users to define extracts through a straightforward configuration process rather than complex development work. To enable the functionality, organisations must activate the feature in Setup Manager under the Manufacturing and Supply Chain Materials Management offering. Oracle Support must also complete the backend activation, and the appropriate security roles must be assigned before users can access the application.

Once enabled, the Data Extraction Tool can be accessed from the Tools menu and is organised around three key areas. Extract Definitions allow users to create and maintain data extraction configurations, selecting the business objects, fields and filters required. Extract Schedules provide a straightforward way to automate the execution of extracts at regular intervals, while Extract Jobs offers visibility of running and completed extractions, making it easy to monitor progress, review results and investigate any issues.

Creating an extract is largely a point-and-click process. Users define an extract name, select an output format (CSV or JSON), choose the required business objects, and specify the fields they wish to include. Filters can be applied to narrow the data set, while column names can be renamed to match downstream integration or reporting requirements.

The tool also supports both Descriptive Flexfields (DFFs) and Extensible Flexfields (EFFs), allowing organisations to include custom attributes alongside standard Fusion data. Extract definitions can be saved for reuse, executed on demand, scheduled for regular execution, or exported between environments.

The Data Extraction Tool supports two execution models:

Synchronous processing is intended for smaller data requests that can be completed immediately. Results are returned within the same transaction, making this suitable for focused data retrieval scenarios. Oracle currently applies a 60-second execution limit.

Asynchronous processing is designed for larger extraction workloads. Users submit a job and receive a job reference that can be used to monitor progress. Once complete, output files are delivered to Oracle Cloud Object Storage or UCM.

Each extract includes metadata detailing the extraction period, row counts and any processing errors, helping administrators validate results and troubleshoot issues.

One of the more interesting additions is Oracle’s Data Extraction Query Transformer Agent. Many organisations have invested heavily in BI Publisher reports and custom SQL queries over the years. The Query Transformer Agent helps modernise these assets by converting SQL into Business Object Query Language (BQL), the format used by the Data Extraction Tool. The agent also validates converted queries against the target schema, helping users identify issues before execution. Oracle has also indicated that future releases will introduce SQL performance assessment capabilities to help organisations identify optimisation opportunities before migration.

RODS coverage continues to grow with each quarterly release. In release 26B, Oracle supports more than 1,100 business objects and has mapped over 600 BICC public view objects. Release 26C is expected to increase coverage to more than 1,650 business objects and nearly 900 mapped BICC view objects, extending support across ERP, HCM, SCM and CX.

Organisations should be aware that business object attribute names may not always match existing ADF View Object or BICC naming conventions. However, output column names can be customised, providing flexibility for downstream integrations and reporting solutions.

Release 26D is set to introduce several important enhancements, including support for custom queries across both synchronous and asynchronous extractions, as well as callback capabilities that enable event-driven processing and notifications.

Oracle is also investing in additional self-service capabilities, including richer extract management, reusable query registration, pre-execution testing and AI-assisted query analysis. As these capabilities mature, the Data Extraction Tool is increasingly positioned as Oracle’s strategic approach to data extraction from Fusion Cloud Applications.

For organisations that currently rely on BI Publisher for integration-focused data extraction, or those finding limitations in existing BICC-based approaches, RODS is worth evaluating. By separating extraction workloads from transactional processing, Oracle has created a more scalable and modern foundation for reporting, analytics and integrations. While coverage is still expanding, the direction is clear: RODS and the Data Extraction Tool are becoming central to Oracle’s long-term strategy for accessing Fusion Cloud data.

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Oracle ERP Cloud Financials 26C

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It’s quarterly release time again, and there are some genuinely strong updates in Financials this quarter. I don’t often cover ERP features, but I did last quarter for the same reason and 26C feels similar. As always, more may follow later in the month, but here’s what’s been announced so far and what’s caught my attention.

There are three Fixed Asset Agents in this release. The new Retirement Request Assistant makes it much easier for asset custodians to initiate and track asset retirements. Using a guided, conversational approach, users can submit requests for assets assigned to them or search for others using identifiers such as serial or tag number. They can capture key details like retirement date and reason, then track progress from submission through to completion. This removes the need for emails and manual coordination, giving users a simple self-service route that speeds things up and improves accountability.

Supporting this, the Retirement Assistant helps finance teams review and process those requests. Fixed asset accountants can manage retirements through the same conversational interface, whether dealing with individual assets, multiple assets or file-based uploads. The assistant guides users through the required steps, validates the data, and highlights failed transactions so they can be corrected and resubmitted without starting again. The result is less manual effort, less rework, and quicker, more controlled processing.

The assistant also improves how exceptions are handled. Finance users can review requests, update key details such as retirement dates or proceeds of sale, and post transactions directly within the same experience. Because everything is handled in one place, there is less need to switch between screens or rekey data, which helps reduce errors. Overall, it creates a smoother end-to-end process while supporting stronger governance as volumes grow.

Alongside this, the Fixed Asset Inquiry Assistant offers a much more intuitive way to access asset information. Users can ask questions in natural language to retrieve details across financials, depreciation and distributions, as well as view current period activity. This makes it easier to understand asset movements, validate transactions and respond to audit queries without relying on multiple reports. Taken together, these assistants represent a clear step forward in usability, helping teams reduce effort while improving visibility and control across the asset lifecycle.

The Budget Adjustment Assistant introduces a more straightforward way for budget office users to manage EPM control budgets. Using natural language, users can create and review budget entries, add or reduce budgets, transfer amounts between accounts or periods, and review balances or previously approved entries. The assistant also flags and helps resolve issues at the point of entry, reducing the likelihood of errors and avoiding rework later.

For organisations, this translates into better efficiency and control. Users no longer need to navigate multiple forms or screens, which speeds up processing and reduces effort. At the same time, built-in validation improves data quality before transactions reach the ledger. The result is faster adjustments, fewer issues, and a more streamlined experience for teams managing complex budgets.

The next two enhancements build on the Expenses Agent introduced previously, extending its conversational, touchless approach into more complex scenarios. Cost allocation now allows users to split expenses across multiple cost centres, projects or tasks directly within the agent. Instead of manually distributing costs across lines, users can simply instruct the agent how to allocate amounts. This improves both accuracy and efficiency, ensuring costs are recorded correctly with far less effort.

Another useful addition is the ability to apply cash advances during expense submission. Employees can select one or more available advances, or choose not to apply them and provide a justification where needed. The agent also handles rejected or withdrawn reports by automatically removing applied advances and notifying the user, helping maintain clarity throughout the process.

Together, these updates strengthen the Expenses Agent by reducing manual intervention and improving financial control. Organisations benefit from more accurate allocations, fewer unapplied advances, and better visibility where advances are not used. Employees benefit from a simpler, more guided process that keeps expense reporting moving and reduces delays across the end-to-end lifecycle.

As always, Oracle may introduce additional ERP Agents later in the month. If anything else stands out, I’ll share a follow-up once the full picture is clearer.

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Oracle’s HCM Professional Concierge: What It Is, How It Works, and Why It Matters for HR Teams

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Oracle has been steadily building out its AI story in HCM Cloud, but the HCM Professional Concierge is one of the first examples that really feels tangible for HR teams. This is not AI added for the sake of it. It is a set of purpose-built, role-aware conversational agents, built directly into the HCM Redwood experience. For me, it stands out as one of the more considered uses of AI Agents in enterprise HR.

If you work in HR operations or as an HR Business Partner, the scenario will feel familiar. A manager wants to understand where their team sits on compensation ahead of a salary review. They open Employment Info, scroll through individual records, try to piece together performance data from one place, compensation history from another, and absence data from somewhere else. It is not a difficult task, but it is a fragmented one. Before long, ten minutes have passed just getting a basic view.

The HCM Professional Concierge simplifies this by bringing everything into a single conversational experience, embedded wherever the HR user is already working. Instead of navigating between screens, they ask a question. The agent brings together the relevant data, guides the next step, and in some cases can even trigger the action directly from the conversation.

It is worth understanding that this is not a single AI agent working behind the scenes. Oracle has taken a supervisor and sub-agent approach, where a top-level Concierge Supervisor receives the user’s query, interprets the intent, and then routes it to the most appropriate specialist agent.

Within the HR Professional Concierge, those specialist agents each focus on a particular area of HR. For example, the Compensation Advisor brings together key information such as compensation data, compa‑ratios, time since the last salary change, and pay grade details for a manager’s direct reports. The Talent Advisor focuses on performance, helping to summarise ratings and support more informed performance conversations.

Other agents support core HR data and processes. The Employment Details Assistant provides access to employment history, assignment information and worker details, while the Leave and Absence Analyst helps identify and manage absence across a team. There is also support for understanding organisational design through the Workforce Structures agent.

In addition, the Concierge can surface policy and guidance through the Policy sub-agent, review personal worker data where needed, and launch reporting through the Reports sub-agent. For broader, team-level insight, the Team Data Hub helps bring data together to support analysis.

What this means in practice is that the user experiences a single, coherent conversation, even though multiple specialist agents may be working in the background to fulfil the request.

So when a manager asks, “show me the most recent performance rating and time since the last salary change for my direct reports”, the Manager Concierge Supervisor recognises that the query spans both compensation and talent data. It then coordinates across the Compensation Advisor and the Talent Advisor behind the scenes. What comes back is a single, joined-up view, rather than two separate outputs that the manager has to reconcile themselves.

That orchestration across multiple agents is where the real value starts to show. Conversational assistants in enterprise applications are not new in themselves. What is more interesting here is the ability to coordinate specialist agents within a single interaction, carry context across the conversation, and route requests intelligently based on both the topic and the data required.

Oracle has introduced three distinct Concierge experiences, each designed around a specific user group and how they typically work. The HCM Professional Concierge is aimed at HR specialists and HR Business Partners. It sits within the HCM Professional Activity Centre, which has become the central workspace for HR service delivery, and supports the sort of queries an HR analyst would usually run. That includes pulling together workforce data for individuals or manager populations, reviewing compensation and employment history, running reports, looking up policies, and guiding HR actions within the flow of work.

The Manager Concierge is focused on line managers who need quick, straightforward access to information about their teams. It brings together compensation, absence, talent and employment data without the need to navigate into individual worker records. The experience adapts based on both the question being asked and the context of the manager’s team, giving them a practical way to not only view information but also complete common HR tasks directly.

The Worker Concierge, meanwhile, is designed for employees themselves. It brings together support for areas such as leave, payroll, benefits and compensation into a single, consistent experience. Behind the scenes, it routes queries to the relevant specialist agent, whether that relates to absence, benefits, pay, or compensation, so the employee does not need to think about where to go to get the answer.

A simple scenario helps bring this to life. A line manager has been told that budget has been allocated for pay rises and promotions across the organisation. Before making any decisions, she wants a clear view of where her team currently stands. Using the Manager Concierge, she can ask a straightforward question in natural language, such as “how long has it been since my direct reports received a pay rise?” The Compensation Advisor returns the answer in a structured, easy-to-read format. She then follows up with a more specific question, “what is Elaine’s compa-ratio?”, and gets a direct response.

Within the same conversation, she can ask for performance ratings through the Talent Advisor and pull through grade information using the Employment Details Assistant. It all happens in one place, without needing to navigate between screens. Multiple specialist agents are working in the background, but from the manager’s perspective it feels like a single, joined-up interaction.

The HR specialist perspective is just as telling. If someone is working on an Employment Info page for a specific worker, they can open the Concierge panel and ask something like, “what is the salary history for Ravi?” or “where is Ravi located?” The response comes back as structured data pulled directly from HCM, without the need to navigate away or open multiple pages.

One question that comes up consistently when Oracle’s AI features are discussed is around data access and security. It is an important one, and the answer here is reassuring. The HCM Professional Concierge works within the same data and functional security model already applied across the HCM Redwood experience. If an HR specialist does not have permission to view a particular employee’s salary in the core application, they will not be able to access it through the Concierge either. There is no separate access layer being introduced. It simply operates within the role-based controls that are already in place.

For organisations working across multiple geographies, the same principle applies. The agent respects the existing configuration of Redwood pages, including any geography-specific policies and legislative requirements. There is also flexibility to tailor how the agent behaves by refining prompts to reflect your organisation’s terminology or local nuances.

The Concierge also sits within a broader shift in how Oracle is shaping the HR user experience. It is alongside the HCM Professional Activity Centre, which acts as a unified Redwood workspace for HR administration. The Activity Centre brings together a more flexible approach to worker search, with filtering, saved views and personalised results. From there, HR specialists can move straight into transactions from a worker’s profile without switching to a separate area. Common actions are surfaced directly in the interface, including access to areas such as the Recruiting Activity Centre, Mass Assignment Change, Mass Legal Employer Change, Payroll Activity Centre and Attendance Violations, which makes it easier to act on information as soon as it is identified.

The Concierge is always present within the Activity Center, giving HR specialists access to conversational support in the context of the work they are already doing.

It also sits within a much broader direction Oracle is taking with role-based, agent-led HR applications. The HR Specialist Workspace is a good example of where this is heading. It builds on the same foundations, but moves towards a Redwood workspace where multiple specialist agents work together to surface relevant insights more proactively.

In that model, the workspace brings together a view of workforce priorities, potential restructuring impacts, compliance alerts, attrition risk and open HR cases. These are drawn from coordinated agent outputs across areas such as Workforce Management, Talent and Learning. The shift here is subtle but important. The agents are not just responding to questions, they are actively identifying what might need attention and presenting it to the user.

There is also a clear emphasis on governance. Audit trails, controls and human oversight are built into how actions are handed off. Oracle is quite deliberate in positioning this around measurable outcomes, with coordinated agent activity and clear decision points. That creates an important distinction from more autonomous AI models. Here, the agents surface and recommend, but people remain firmly in control of decisions and actions.

From an implementation perspective, the HCM Professional Concierge and its supporting agents are delivered as part of Oracle HCM Cloud Release 26C. There is no need to build these capabilities from the ground up. They are available out of the box, with the ability to adapt behaviour through prompt configuration so that it reflects your organisation’s terminology and ways of working.

As ever, I will keep a close eye on how this develops across the HCM suite and share updates as new capabilities emerge. If you are starting to think about how this fits into your wider HCM AI strategy, or you are planning for a 26C upgrade, now is a sensible point to begin that conversation.

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