Tag: Agentic Apps

Oracle Collector Workspace: Could AI Finally Transform Collections from a Chasing Process into a Strategic Function?

~ K8 Mead ~ Leave a comment

For many finance teams, collections remains one of the most manual activities in the order-to-cash process. Collectors spend their days switching between screens, reviewing ageing reports, checking customer histories, chasing updates from colleagues and deciding who to contact next. The challenge has never really been a lack of data. Most organisations have plenty of it. The problem is knowing which information matters, which customer needs attention now and what action is most likely to improve the outcome. That is exactly the challenge Oracle is aiming to address with the new Collector Workspace Agentic Application in Oracle Fusion Cloud ERP.

One of the themes running through Oracle’s latest Agentic Applications is a shift from helping users complete tasks to helping organisations achieve outcomes. Rather than presenting information and leaving users to work out what to do next, these applications continuously monitor data, identify priorities, recommend actions and help drive work forward.

Collector Workspace applies this approach to collections management. The objective is straightforward: improve cash collection performance whilst reducing the effort required from collections teams. Oracle states that the application is designed to support more predictable cash collection, reduced DSO, stronger customer engagement and higher collector productivity. What makes this interesting is that the application doesn’t rely on a one-size-fits-all AI model. Instead, it operates within the framework of an organisation’s own collections policies.

Perhaps the most significant aspect of Collector Workspace is the role of the Collections Policy Document. Rather than allowing AI to make arbitrary decisions, organisations define the rules that drive prioritisation and recommended actions. The policy document can include business metrics, prioritisation criteria, allowed actions and guidance for next best actions in different collection scenarios.

For example, a business could define that customers with a high percentage of overdue balances should receive immediate attention, while lower-risk accounts are handled differently. The application uses these rules to prioritise work and recommend actions. This should feel reassuring to finance leaders who are interested in AI but remain concerned about governance and control. The AI is not replacing established collections processes. It is helping teams execute them more consistently.

One of the reasons collections can be inefficient is the amount of context switching involved. Collectors often need to review payment history, disputes, promises to pay, customer communications and account status before deciding how to proceed.

Collector Workspace brings this information together into a single view, providing account snapshots and historical interaction data in one place. This alone could save significant time. But Oracle has gone further by adding conversational AI capabilities that allow collectors to ask natural language questions about customer accounts and transactions directly within the workflow. Instead of navigating through multiple screens to locate information, collectors can simply ask questions and receive answers within the context of their work.

Another area where Collector Workspace stands out is customer engagement. The application can generate contextual emails and AI-assisted call scripts, helping collectors communicate more consistently and efficiently. It can also create Promise-to-Pay requests and support follow-up activities.

This isn’t about replacing human interaction. Collections often requires judgement, negotiation and relationship management. Instead, the technology aims to remove preparation effort so collectors can focus their attention on the conversation itself.

For organisations with large collections teams, consistency can sometimes be difficult to maintain. AI-generated communications may help ensure that messaging remains aligned to company policy and best practice.

One capability I find particularly compelling is incoming email intelligence. Collector Workspace can summarise customer emails, detect intent and convert responses into actionable follow-up items. It can also identify Promise-to-Pay commitments contained within customer communications.

Anyone who has spent time in finance operations knows just how much effort can be consumed interpreting emails, updating records and determining the next step. Automating these administrative activities could have a meaningful impact on productivity.

Oracle’s roadmap suggests this capability will continue to grow, with future plans to identify additional intents such as disputes, invoice copy requests, purchase order updates and contact corrections.

What interests me most about Collector Workspace is what it represents. For years, ERP innovation has focused on making transactions faster and processes more efficient. Agentic Applications feel different. They are designed around outcomes rather than transactions.

In the case of collections, success is not measured by how quickly someone can create an activity record or send an email. Success is measured by improving cash flow, reducing overdue balances and helping collectors focus on the accounts that will have the greatest business impact.

Collector Workspace is one of the clearest examples yet of how Oracle is applying agentic AI to solve a real business problem. If it delivers on that promise, collections teams may spend less time deciding what to do next and more time achieving the outcomes the business actually cares about.

And perhaps that is the real story here. The future of enterprise AI may not be about doing the work for us. It may be about helping us focus our expertise where it delivers the greatest value.

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

Oracle’s Ledger Agent Is Evolving: What You Need to Know About the New Ledger Agentic App

~ K8 Mead ~ Leave a comment

If you’re already using Oracle’s Ledger Agent, there’s an important change arriving with the Oracle Fusion Cloud ERP 26C release. Oracle has introduced the new Ledger Agentic App, which represents the next stage in the evolution of AI-assisted accounting within Fusion. While many of the capabilities will feel familiar, Oracle’s long-term investment is now focused on the Agentic App, and a retirement timeline has been confirmed for the existing Ledger Agent. The good news is that there’s plenty of time to prepare, and getting started is simpler than you might expect.

The Ledger Agentic App builds on the foundations of Ledger Agent and brings together accounting insights, proactive monitoring and conversational AI in a dedicated Ledger Workspace. For existing users, the experience will feel familiar. You can still ask questions using natural language and receive financial insights without needing to navigate reports or dashboards. What changes is the overall experience. Rather than offering standalone AI interactions, Oracle is creating a single workspace where accountants can investigate issues, review insights and interact with AI in one place.

In the 26C release, Oracle has enhanced the application’s understanding of accounting terminology and business context, helping it deliver more accurate responses and maintain the flow of conversations more effectively when users ask follow-up questions. This is more than a simple name change. The Ledger Agentic App provides the platform Oracle will use to deliver future accounting-focused AI capabilities.

What happens to the existing Ledger Agent? There’s no immediate pressure to switch. Oracle has confirmed that Ledger Agent will continue to be supported during both the 26C and 26D releases. However, it is scheduled for retirement in 27A, which means organisations currently using Ledger Agent should start planning their transition. 

Oracle recommends evaluating the Ledger Agentic App during 26C and completing the move during 26D, ensuring everything is in place well before Ledger Agent reaches end of life. Importantly, any monitoring prompts and generated insights you’ve already configured within Ledger Agent will remain available through the new Ledger Workspace, making the transition considerably easier. 

One of the most important considerations is licensing. For production environments, the Ledger Agentic App requires Oracle’s Agentic Apps SKU, which is licensed separately from your standard Oracle Fusion subscription. The Agentic Apps licence provides access not only to the Ledger Agentic App, but also to a growing portfolio of agentic applications across Oracle Fusion, including areas such as collections, payables close and billing operations. It also includes access to Oracle’s AI App Builder and agent orchestration capabilities, along with an annual allocation of AI units that can be shared across applications. Licensing models and pricing can vary, so it’s worth discussing your options with your Oracle Account Director to understand what this means for your organisation. 

The good news is that Oracle allows customers to explore the Ledger Agentic App in non-production environments without purchasing the Agentic Apps licence. This provides an opportunity to evaluate the experience, understand the potential benefits and prepare for the transition before making any production licensing decisions. 

For organisations already using Ledger Agent, enabling the Agentic App is straightforward. There’s no requirement for additional security roles or data access configuration. Administrators simply need to enable the Ledger Agentic Application feature through the General Ledger functional area’s feature opt-in settings. 

Once enabled, users can access both the existing Ledger Agent and the new Ledger Workspace, providing a smooth transition period while teams become familiar with the new experience. If you haven’t yet adopted Ledger Agent, Oracle’s recommendation is even simpler: skip directly to the Ledger Agentic App. 

The introduction of the Ledger Agentic App offers a glimpse into Oracle’s wider vision for the future of finance operations. Oracle is investing heavily in AI capabilities that help finance teams move beyond basic enquiries and into areas such as exception management, root cause analysis and guided decision-making. Future enhancements are expected to help accountants investigate variances, resolve accounting issues and receive recommendations based on organisational policies and financial context. The aim is not to replace finance professionals. It’s to reduce the time spent searching for information and investigating routine issues, allowing teams to focus on higher-value analysis and decision-making. If your organisation is already benefiting from Ledger Agent, the new Ledger Agentic App is a natural next step. 

With support for Ledger Agent continuing through 26D, there is no urgent deadline. However, the transition period provides an ideal opportunity to explore the new experience, understand the licensing implications and begin preparing for Oracle’s future direction. The Ledger Agentic App is clearly where Oracle’s investment is focused, and organisations that start evaluating it now will be well positioned to take advantage of the new capabilities arriving over the next few releases. 

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

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

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.

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

~ K8 Mead ~ Leave a comment

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.

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.

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

Oracle Fusion App Builder: Streamline Your Agentic Applications

~ K8 Mead ~ Leave a comment

Over the bank holiday weekend, with the heat driving me indoors, I opened up my Fusion demo environment and decided to try building my first Oracle Agentic Application. Within a few minutes, it was up and running. That is not marketing spin, it is genuinely how quick and straightforward the experience was. It also gave me a good reason to sit down and share what I found.

Officially, the Agentic Application Builder does not arrive until Release 26C. However, if you are already familiar with AI Agent Studio and have access to a non production pod, there is more than enough available today to start building something meaningful and to get a feel for where this is going. Over the weekend, I put together two applications. The first used one of Oracle’s out of the box prompts, and the second was based on a custom prompt I created with support from Microsoft Copilot. Both came together quickly, which is really the point.

Before getting into what I built, it is worth being clear on how this fits together architecturally. AI Agent Studio, which is included within your Fusion Applications subscription, will introduce the Agentic Application Builder in Release 26C. It provides a low code way to create and extend agentic applications directly within Fusion. What makes it different is the starting point. Rather than beginning with code or a process diagram, you simply describe the business outcome you want to achieve, and the builder identifies the right agents, creates the initial structure, and connects to your enterprise data. It is also important to understand the licensing model. While you can explore, test, and build in non production environments without any additional cost, a separate licence is required to deploy and run these agentic applications in production. This means there is nothing to stop you getting hands on now and understanding the art of the possible before making any investment decisions.

Applications built in this way run natively within Fusion Applications, using your existing business objects and data, and operating under Fusion’s role based security. That is worth pausing on, because it means you are not creating something separate or bolting on additional functionality. These agents are working within the same security and access model your users already rely on in their day to day roles.

The builder brings applications together using reusable agent teams. These can be provided by Oracle, developed by partners, or created in house to suit your own needs. Each team is designed to handle a specific role, and the builder assembles them into a single application that works towards a common business outcome.

For my first build, I started with one of Oracle’s out of the box example applications. From selecting the template to having a working framework in front of me took only a few minutes. The App Builder presents a range of example agentic applications from the outset, giving you something tangible to work from straight away. You can select one, use it as a foundation, adapt it to your needs, and build from there. In my case, I chose the Talent Review and Insights application.

The steps are: go to AI Agent Studio, open the Apps tab, select Add, enter the name, code, and description for your agentic app, and navigate to the App Builder. Select one of the example apps and you’re looking at a working framework almost immediately.

That speed was the first thing that stood out to me. The framework clearly sets out the agent teams involved, the different sections of the application, and how everything fits together. You are not faced with a blank canvas. Instead, you can immediately see which published workflow agent teams are available to include, and the structure gives you a clear sense of how the application will operate before you have made any changes.

What really caught my attention was the quality of the insights it produced. This is not a static report. The agents actively draw on the data in your environment and present findings in a way that is designed to prompt action, not just provide information. For an HR practitioner used to working with standard Talent Review dashboards, the difference in how those insights are surfaced is immediately noticeable.

The second build is the one I found most interesting, and it was just as quick to put together. I used Microsoft Copilot to help shape a detailed natural language prompt, then passed that into the App Builder through the Ask Oracle interface to generate a completely custom agentic application from scratch.

The prompt set out an application designed for Payroll Administrators, bringing everything into a single workspace to monitor payroll activity and improve processing accuracy. The aim was to give payroll teams a clear, action focused view of exceptions, anomalies, and key changes that need investigation before payroll is finalised. In practice, that means removing the need for administrators to piece together that picture across multiple pages and reports.

The App Builder works through three clear phases: intent, assembly, and refinement. You start by describing the business objective in plain language, the builder then suggests the most relevant agents and proposes an initial structure, and from there you refine the application through layout changes, naming, and added detail before publishing. The whole journey, from a simple prompt to a structured application framework, moves quickly. If anything takes time, it is shaping the prompt itself rather than waiting for the builder to respond.

What I found is that the quality of the prompt makes a real difference to what the builder produces. The prompt I created with Copilot was clear about the user persona, the business context, and the type of information needed, focusing on a Payroll Administrator working in a pre finalisation scenario and looking for exceptions, anomalies, and priority changes. The application that came back reflected that level of clarity. In many ways, it is no different to working with any AI tool. The prompt is the critical part. The clearer and more specific you are, the more useful and relevant the outcome will be.

For those looking to get familiar with the structure ahead of 26C, an agentic application is built from three core elements: agent teams, communications, and actions. Agent teams sit at the heart of it. Only published workflow agent teams that have been enabled for use in applications are available to select, which helps ensure consistency and control over how these applications are put together.

Communications allow the application to send emails and messages using predefined templates. These templates can take the form of PowerPoint, PDF, email, or simple text. For email templates, the agent can be given the ability to suggest recipients, generate a subject line, and complete sections of the content. For PDF and PowerPoint templates, the agent can generate titles and populate the content, helping to streamline how information is produced and shared.

Actions define what happens as the application runs, including where human approval is needed along the way. The flow itself is straightforward. A widget or user interface element triggers a command, that command determines which action to run, and the action then executes its steps in sequence. There is a good level of flexibility in how those steps are defined. You can keep an action visible in the interface after it has run, navigate users to another application, send a command to an agent, refresh what the agent is showing, or switch the application context. Taken together, these steps allow you to shape how the application behaves and how users interact with it.

Once built and tested, you publish the app. Users can then access it from the AI Agents page, reached via Me > Quick Actions > Show More > AI Agent Studio > AI Agents.

I realise I am starting to sound a bit like an Oracle advert, so it is worth being honest about the experience. In its current pre release state, not everything behaves as you would expect from a finished product. Some agent team options are not yet fully populated, and there are limits to how far you can test the end to end flow in a sandbox without complete data. That is to be expected at this stage.

What is clear, though, is the direction of travel. The App Builder is designed to enable functional consultants and technically minded administrators to create agent driven applications without writing code, and to do so quickly. Starting with natural language removes much of the usual barrier, building from reusable agent teams means you are not starting from scratch each time, and the inclusion of example templates means you can have something up and running in the time it takes to make a coffee. For organisations investing in the Fusion Agentic Apps Platform, this is where a great deal of tailored capability is likely to be developed over the coming releases.

If you have access to a non prod pod and want to get ahead of 26C, it is well worth spending some time in AI Agent Studio now. The core concepts you will be working with, including agent teams, sections, communications, and actions, are already in place and align with what will be available in the full release.

I will share a more detailed walkthrough once 26C is live and the full feature set is available. In the meantime, if you are interested in where this is heading, it is worth taking a look at my earlier write up on AI Agent Studio and what it means for Oracle Fusion HCM.

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

Reinventing How Work Works: The Business Case for Oracle Fusion Agentic Applications

~ K8 Mead ~ Leave a comment

Oracle has been making a clear and increasingly consistent argument over the past few months: enterprise software has reached the limits of what a system of record can do. I’ve written before about the introduction of Agentic Applications at AI World London, and about the specific HCM applications that were announced alongside them. But there’s a broader story here that I haven’t fully explored yet, and it’s one that I think matters for every Fusion customer, not just those focused on HCM.

This post draws on the “Reinventing How Work Works” webinar, which stepped back from individual applications and made the architectural and commercial case for why the shift to agentic is happening, what it actually looks like in practice, and where Oracle is taking this next. If you’re trying to build internal momentum for agentic adoption, or if you’re trying to explain to a leadership team why this is different from previous AI announcements, this is the post to share.

The framing that Oracle used throughout this webinar is, I think, one of the clearest explanations of what has actually changed. Traditional enterprise systems, including Fusion as it has historically operated, are systems of record. They follow fixed rules, capture what happened, retrieve information when asked, and complete transactions. They document the business. What they don’t do is run the business.

Agentic Applications represent a move to what Oracle calls systems of outcomes. Rather than waiting for a person to interpret data and decide what to do next, a system of outcomes works toward objectives, makes things happen, solves problems, and achieves results. The underlying system of record doesn’t go away. The data, governance, approval hierarchies, role-based access control, and audit history are still there, and in Oracle’s case, they’re still the source of truth for every transaction. What changes is the layer operating on top of that foundation.

This architecture diagram is worth studying if you haven’t seen it. Agentic Applications sit in a new composable layer above the existing ERP, HCM, and CX transactional applications. That layer is powered by teams of AI agents coordinated through Oracle AI Agent Studio, drawing on the full enterprise data model, security model, and process history that already exists in Fusion. Beneath all of this, Oracle Cloud Infrastructure (OCI) provides the AI data platform, and a range of large language models (LLMs), including those from OpenAI, Cohere, Meta, Anthropic, xAI, and Google, are available depending on the task and preference.

Every Fusion Agentic Application is built around four core dynamic areas. Understanding these is useful when you’re evaluating a specific application or explaining the concept to stakeholders.

The first is the Advisor, which is the “Ask Oracle” conversational interface. This is where a user can ask natural language questions and get contextual, data-aware responses rather than navigating to a report. The second is the Information Summary, which provides an intelligent, prioritised view of what’s happening right now in that area of the business, surfaced automatically rather than requiring the user to run queries. The third is Priority Actions, a curated queue of recommended next steps that the agents have identified based on current conditions, risk signals, and business objectives. The fourth is Communications, which handles notifications, responses, and outbound actions within the appropriate governance boundaries.

These four areas appear consistently across all 22 applications, which is deliberate. Oracle’s position is that once a user understands the structure in one application, they can navigate any other agentic application without relearning the interface.

One of the most practically useful concepts introduced in this webinar is what Oracle calls the Autonomy Dial. It’s a spectrum with three positions, and it addresses one of the most common concerns I hear from customers and consultants: how much control do we give up?

At the “Human in the Loop” end, the agent assists and a person decides. The agent drafts, recommends, and prepares; the human reviews and approves. This builds trust, improves speed and consistency, and keeps people firmly in control. The business impact is described as immediate productivity gains.

In the middle is “Human in the Lead”, where the agent executes and a person monitors. The agent handles routine work and manages to policy; a person steps in for genuine exceptions. This scales output without adding headcount and frees teams for higher-value work. The impact here is scaled operations.

At the “Autonomous Execution” end, the agent drives and a person owns. End-to-end execution happens within policy, continuous real-time optimisation takes place, and human involvement is reserved for true exceptions. The impact is described as business transformation.

What I find compelling about this model is that it isn’t prescriptive. Oracle isn’t saying every organisation should start at one end or aim for the other. Each position on the dial represents a valid operating model depending on the process, the risk tolerance, and the maturity of the organisation. A payroll close process might comfortably sit at Human in the Lead. A workforce scheduling decision for a critical shift might warrant Human in the Loop until confidence is established. A high-volume procurement matching task might be a good candidate for Autonomous Execution relatively quickly.

My earlier posts covered the eight HCM applications in detail. The full announcement of 22 applications in releases 26B / 26C spans ERP/SCM, HCM, and CX, and it’s worth understanding the breadth of this, because it signals how Oracle is positioning agentic across the entire Fusion suite rather than as an HCM-specific capability.

On the ERP and SCM side, the applications include Design-to-Source Workspace, Product Readiness Workspace, Production Shift Operations Workspace, Sales Order Command Centre, Batch Process Manufacturing Workspace, Logistics Execution Command Centre, Maintenance Operations Workspace, Warehouse Operations Workspace, Cost Accounting Close Workspace, Sourcing Command Centre, Collectors Workspace, and Security Command Centre.

The Design-to-Source Workspace is a useful example of the transformation logic. Previously, product design and bill of materials work happened in separate systems. Sourcing relied on items entered manually. Negotiation delays accumulated when information was missing or unresolved. With the agentic application, product specifications translate automatically into qualified supplier lists, bills of materials are generated directly from CAD files, at-risk negotiations are flagged automatically, and bids are evaluated across cost, lead time, quality, and risk in a single view. The outcome is faster time to market and improved sourcing cycle times.

On the CX side, three applications have been announced: Cross-Sell Program Workspace, Contract Compliance Workspace, and Sales Command Centre. For CX teams, the Sales Command Centre in particular brings together the kind of deal health monitoring, risk flagging, and next-step recommendation that previously required significant manual analysis across multiple reports.

I’ve written in detail about Oracle AI Agent Studio in previous posts, but the webinar highlighted several new capabilities that are worth calling out specifically, because some of them genuinely change what’s possible for teams building custom agentic applications.

The most significant new addition is the Agentic App Builder, which is released in 26C. This is what Oracle describes as a “no-code agentic brain”: you describe your objective in natural language, the system explains and builds the workflow, generates agents and the underlying code automatically, and allows you to diagnose and fix issues in real time. In the demo, a user types a description of a sales opportunity health and risk management app, and within moments a structured agentic application is assembled from reusable agents, with a Deal Summary Agent, a Risk Agent, a Customer Insights Agent, and a Process Agent already in place and connected. It’s a significant step forward from the existing builder experience.

Alongside this, several other capabilities have been marked as new in the current release: Workflow Orchestration, Content Intelligence, Contextual Memory, Multi-Modal support, an Agent ROI Dashboard, and enhanced Security, Auditability, and Governance controls. Contextual Memory is worth paying attention to particularly, because it allows agents to retain information across interactions, which is what enables genuinely personalised, continuous support rather than stateless responses to each individual query.

The studio now also supports full interoperability through MCP (Model Context Protocol) and A2A (Agent-to-Agent) protocols, which means agents built in Fusion can exchange context with agents or tools running outside the Fusion estate, provided the appropriate governance controls are in place.

One thing the webinar made very clear is that Oracle isn’t building this alone. The Fusion AI ecosystem now includes 73,400 certified builders, 10,000 developers actively building agents, and over 100 pre-built agent templates in the AI Agent Marketplace, which is now open to all partners for submissions. Open standard support includes native MCP integration across connectors and an agent-to-agent registry within Oracle AI Agent Studio itself.

For customers, this matters because it means the pool of available agents and expertise is growing rapidly. You don’t need to build everything from scratch, and you don’t need to rely solely on Oracle to extend the platform. The open partner submission model for the marketplace is a meaningful shift, and it’s one that will accelerate the availability of domain-specific and industry-specific agents over the coming months.

The summary that Oracle closed with is a useful way to frame internal conversations: Fusion is moving from systems of record to systems of outcomes. Agentic Applications get work done. Oracle AI Agent Studio lets you build, deploy, and scale agents specific to your organisation. OCI AI Advantage runs it all securely at scale.

What I’d encourage any Fusion customer to take from this is that the window to start is now. The pricing model has already been simplified significantly (covered in my earlier post), the tooling to build and extend has matured substantially, and the evidence base from production deployments is solid. Starting with one application in one process area, positioned at Human in the Loop on the autonomy dial, is a low-risk, high-value entry point that builds organisational confidence while delivering measurable results.

If you’re thinking about where to start or how to make the case internally, I’m happy to talk it through. In the meantime, why not check out my earlier post on the HCM-specific Agentic Applications announced at AI World London? You can find it here. And if you missed the original announcement post covering the architecture, the maturity model, and the updated pricing, that’s a useful starting point too, and you can find it here.

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

Agentic Applications for HCM Cloud

~ K8 Mead ~ 1 Comment

At the AI World London HCM Partner Summit, Oracle unveiled 22 new Agentic Applications across the Fusion suite, including eight designed specifically for HCM Cloud. One of the standout additions is the Workforce Operations Command Centre, which brings scheduling, time, and absence management into one coordinated hub. It highlights real‑time risks, helps managers make confident coverage decisions, and streamlines day‑to‑day operations. During the demo, we saw a live priority queue flagging shift conflicts and timecard issues by severity, with simple one‑click options to approve, reassign, or review — making it far easier to stay ahead of workforce challenges.

Oracle has also introduced a series of new workspaces designed to streamline everyday manager and employee tasks. The Hiring Workspace for Store Managers brings candidate details, interview scheduling, and urgent hiring requests together to support faster decisions, while the Manager Concierge Workspace unifies compensation, performance, talent, and absence insights with simple, policy‑backed actions. The Team Learning Workspace helps managers stay ahead of compliance risks and focus on development priorities, and the Career Advancement Command Centre connects employees to suitable roles, required skills, and training. Alongside this, the My Help Workspace offers a clear view of open requests and relevant knowledge articles, and Contracts Intelligent Counsel, also known as Agentic Compliance, provides continuous, autonomous monitoring of contract terms and policy changes to reduce compliance overhead.

Oracle also unveiled Oracle Manager Edge, a new personal AI coach designed to give managers practical, data‑driven guidance directly within Touchpoints, with suggested actions seamlessly linked to Oracle Team Touchpoints. Although it isn’t an Agentic Application, it will be available through the AI Agent Studio once released, offering organisations an accessible way to bring personalised, context‑aware coaching into everyday management without additional complexity.

Oracle also confirmed six dedicated Payroll Agents designed to cut manual effort and improve payroll accuracy. The Payslip Analyst, already live in 25D, helps employees resolve payslip queries and has been shown to reduce inquiry costs by up to 70 per cent with a rapid ROI. The Compliance Update Agent (26C) converts legislative changes into proactive configuration updates, removing up to 90 per cent of the manual workload. The Court Order Processing Assistant (26A) fully automates garnishment intake, while the Tax Calculation Statement Agent (26C), currently specific to the US and California, explains the detailed tax logic behind each payroll run. The W‑4 Compliance Agent (26B) automates US tax‑form completion, and the Pay Run Agent (26C) provides real‑time summaries and flags exceptions, reducing manual review efforts by as much as 70 per cent. For UK and global payroll teams, the Payslip Analyst and Compliance Update Agent are the most relevant today, with the remaining agents focused on US‑specific requirements.

As Oracle continues to expand its portfolio of Agentic and AI‑driven capabilities, the direction is clear: more guidance, more automation, and less friction across everyday HR and payroll operations. For organisations already using Fusion, these new applications offer a practical way to improve decision‑making, strengthen compliance, and deliver a smoother experience for managers and employees alike. And with more innovation on the horizon, now is an ideal time to explore how these tools can support your roadmap and help your teams work smarter, not harder.

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

Under the Hood: How Oracle’s Workflow Agents Actually Work

~ K8 Mead ~ 1 Comment

After sharing my initial thoughts on the Oracle AI World announcements earlier this week, I’ve since taken a closer look at what sits behind the headlines. The announcements focused on what Oracle is delivering, but what really interests me now is the how. That is where things get genuinely exciting for those of us who will be hands-on, building and configuring these new capabilities.

One thing that really helped me make sense of Oracle’s approach was the clear distinction between workflow agents and hierarchical agents. They serve very different purposes, and treating them as interchangeable would quickly lead to the wrong outcomes. Workflow Agents follow policy‑bound orchestration with contextual reasoning and are designed for predictability, auditability and stable SLAs, making them ideal for things like payroll deductions, purchase requisitions or leave approvals where governance and consistency are essential. Hierarchical Agents work differently, using LLM‑led decomposition with specialist sub‑agents, which makes them a better fit for open‑ended problems with many possible paths where multi‑domain reasoning matters more than repeatability. Oracle has intentionally designed the two to complement each other, with Workflow Agents providing the structure by defining stages, approvals, retries and SLAs, while Hierarchical Agents take on the heavier analytical or generative work within specific steps. The result is a balanced model that preserves governance while still giving teams the flexibility to tackle more complex reasoning tasks.

Oracle has outlined seven composable design patterns for building Workflow Agents, each suited to a different type of process. Chaining uses sequential intelligence to pass enriched context from one step to the next, which works well for extract‑validate‑decide‑act processes. Parallel execution allows multiple branches to run at the same time and then consolidates their outputs into a single decision, making it a strong fit for compliance or risk scenarios. Switch flows use context‑aware decisioning to route work based on intent, profile, state and policy; for example, an employee updating deductions after a new baby can trigger both Benefits and Payroll updates automatically with no handoff. Iteration supports adaptive refinement by recalculating until constraints are met, which suits planning and scheduling tasks. Looping introduces self‑correction, such as regenerating and revalidating an invoice when OCR results do not match. RAG‑assisted Reasoning retrieves the right policy information before applying thresholds or routing logic. Finally, timer‑based execution triggers actions on a schedule, such as checking invoice status and notifying the accounts payable owner before an SLA is at risk.

The Workflow Agent canvas in AI Agent Studio groups its building blocks into four areas that shape how an automation behaves. AI nodes include LLM, Agent, Workflow and the RAG Document Tool. Data nodes cover things like the Document Processor, Business Object Function, External REST, Tool and the Vector DB Reader or Writer. Logic nodes provide Code and Set Variables, while the Workflow Control nodes handle governance through Human Approval, If Condition, For Loop, While Loop, Switch, Run in Parallel, Wait and Return. At workflow level, the Triggers tab supports Webhook, Email and Schedule triggers, and the Error Handling section lets you notify recipients by email if a workflow reaches a permanent failure, using context expressions such as $context.$workflow.$traceId. For image-related tasks, the Vision LLM node is the correct choice, although it is classed as a premium tool and comes with associated pricing considerations.

METRO, Oracle’s monitoring layer for Measurement, Evaluation and Testing for Real‑time Observability, gives teams a clear view of what their Workflow Agents are doing across inbound emails, approvals and scheduled runs. From the 26C release, it will also surface AI Unit consumption, which becomes increasingly important as organisations scale their use of agents and need tighter visibility and cost control.

Pricing has been a major consideration for customers exploring AI Agents, and the new structure aims to simplify things through the introduction of AI Units, or AUs. Oracle is expected to publish the full details in April or May, but the core concept is that an AU costs roughly $0.01 and is calculated as: AU consumption = CEILING((Input Tokens + Output Tokens) / 10,000) × Action Value Factor. The Action Value Factor varies depending on the action type and the LLM tier being used. General actions such as Q&A, approvals and reasoning have a 0x factor on the Basic LLM, while Premium and Bring Your Own apply higher factors. Artifact creation and audio generation sit in higher tiers again, with video generation marked as coming soon. Every Fusion customer receives 20,000 AUs per month at no charge, pooled across all pillars with unused units rolling over to the end of the contract term. Additional AUs are available in $1,000 increments.

What I find most compelling about this architecture is that it’s built for the realities of enterprise work rather than an idealised version of it. The self‑correction loops, governance controls, evaluation framework and hybrid agent pattern all acknowledge that real business processes can be messy and that auditability is essential. The 22 new agentic applications arriving in 26B across ERP, HCM, SCM and CX give us a clear benchmark for what good looks like in practice. If you’re interested in exploring how Workflow Agents could support your organisation’s processes, now is a great time to start that conversation.

In the meantime, why not check out my earlier post covering the Oracle AI World announcements? You can find it here.

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