Oracle's AI agents can decide and act autonomously, but who's liable when they fail?

Oracle: AI agents decide and act. Liability question remains

Fusion Agentic Applications promise autonomous enterprise decisions. Gartner urges caution Oracle says it's building a suite of AI agents binto its cloud-based enterprise applications, claiming they can make and execute decisions autonmomously within business processes. But analysts are urging caution given unresolved questions around data integration and liability. Unveiled in London this week, Fusion Agentic Applications will be integrated with the Oracle Fusion Cloud Applications suite, covering financials, ERP, HR, payroll and supply chain management. Oracle argues it has a structural advantage here: the data needed to train and run these agents already lives inside its enterprise applications. "Applications that can reason, decide, and act in pursuit of defined business objectives," is how Big Red's application development executive veep Steve Miranda framed the shift, a move away from process-focused software toward outcome-driven automation. Oracle, for example, promises a Design-to-Source Workspace Agentic Application, which it says can work across engineering, supplier, and sourcing decisions to create one "coordinated and continuous process." However, Balaji Abbabatulla, Gartner vice president and vendor lead for Oracle, was more measured, pointing to unanswered questions about how the technology will be implemented in an enterprise setting. "Our position is that this sounds good, but be cautious. It doesn't necessarily look as glittery as it sounds. There are challenges under the hood which are not being overcome right now, but maybe over time," he said. In January, Gartner said boards of global businesses are putting tech teams under pressure to implement AI agents. Application, database, service layer, and cloud vendors are all scrabbling over the expected bonanza, trying to build influence over enterprise AI strategy. Oracle's pitch is to house AI agents within its enterprise application suite, and sell AI Agent Studio for Fusion Applications to help organizations build, connect, and run AI automation and agentic applications. Oracle has also launched an AI Data Platform to integrate data from different sources to build AI agents. Gartner's Abbabatulla said that via the Platform Oracle wants to connect non-Oracle repositories, legacy applications - such as SharePoint repositories - and extract information from them. Although Big Red provides tools for data or technology experts to do that, it is not automated. "There's no kind of autonomous way of synchronizing these different data repositories in the background," he said. Building agents to run application-based processes will require a lot of work - and most likely spending money with Oracle to get the right engineering expertise, he added. That's a hurdle for some large enterprises already invested in data platforms from Databricks, Snowflake, Cloudera or other vendors, with some initiatives harking back to the "big data" investment era. Abbabatulla sees Oracle's pitch as partly defensive, using data-in-context as an incentive to keep customers within its ecosystem. "The transition overhead is massive, because these are investments people have made for years now," Abbabatulla said. "This is unlikely to actually attract them to let go of this investment, but I'm sure there'll be organizations willing to try this in addition to some of those other investments they have made." Oracle and other vendors must still answer the question of who takes responsibility for AI decision-making should it go wrong, a problem The Register has been raising for a couple of years. If an AI agent makes a bad decision at scale and speed, cascading errors could spread before anyone notices. Oracle's answer so far is monitoring and audit tooling, but Abbabatulla is unconvinced: "I don't see a clear response from any vendor on the liability issue." Mickey North Rizza, IDC group vice-president enterprise software, was more bullish, calling it a "significant shift" in agentic systems as they continuously complete work within the enterprise software system. "Overall, this is a great move for Oracle positioning it as a market shaper towards the Agents as Apps. It won't be the app with the best UI that does well, but rather the agent that reliably completes outcomes that are at scale, with trust and bring sustained economic leverage," she said. With boards pressuring tech teams to deploy agents, Oracle, like every major platform vendor, is fighting for a piece of that pie. ®

Oracle is revamping how businesses procure AI agents -- leave the invoices to AI while you handle the negotiations

* Oracle's new AI agents let automation handle some low-value work so humans can focus on negotiations * The AI Agent Studio is also easier to use with a new no-code, natural language agent builder * Share prices are up slightly, but investors are still worried about traditional software's longevity Oracle is looking to inject even more agentic AI into enterprise software, leaving low-value admin work like invoicing, purchase orders and data entry to the computer so that humans can focus more on negotiation, strategy and risk decisions. The news comes as part of a broader revamp of the Fusion cloud apps announced at its Oracle AI World Tour London event, but it also responds to growing concern over the role of traditional software in the modern workplace. Investors are already worried about existing types of software, with company shares down around 20% year-to-date, but the company is fighting back with more AI tools. Oracle is adding more AI to its software Oracle has added 22 new Fusion Agentic Applications across finance, HR, supply chain and customer experience to help realize four goals: fewer payroll issues, lower supplier sourcing costs, lower customer acquisition costs, and faster cash collection. Fusion Agentic Applications are described as teams of AI agents that can reason, decide and act, just like a team of human workers, but they're built directly into transactional systems rather than being copilot-style add-ons. "With Fusion Agentic Applications, we are moving enterprise software beyond passive systems of record and providing our customers with applications that can reason, decide, and act in pursuit of defined business objectives," Applications Development EVP Steve Miranda wrote. At the same time, Oracle has also updated its AI Agent Audio to include a no-code, natural language agent builder as well as a dedicated ROI dashboard. Applications Development EVP Chris Leone explained that companies need this support to fine-tune their agents based on workflows and priorities. Follow TechRadar on Google News and add us as a preferred source to get our expert news, reviews, and opinion in your feeds. Make sure to click the Follow button! And of course you can also follow TechRadar on TikTok for news, reviews, unboxings in video form, and get regular updates from us on WhatsApp too.

Oracle's new AI bet: Make the AI database the center of agentic workloads - SiliconANGLE

Oracle's new AI bet: Make the AI database the center of agentic workloads In the race to define enterprise artificial intelligence, most of the industry is looking up the stack -- chasing smarter models, bigger benchmarks and more capable generative AI systems. In some ways, Oracle Corp. is looking in a different direction. At its latest showcase during the Oracle AI World Tour London 2026, the company made a calculated move in the agentic AI market. Instead of competing head-on in the model wars, Oracle is positioning the database as the center of gravity for enterprise agentic AI, effectively arguing that the future of AI won't be determined by agents alone, but by where and how they interact with data. It's a familiar posture for Oracle, a company that has historically won by controlling the system of record. But in the context of AI, the implications are larger. The database, long treated as back-end plumbing, is being recast as something closer to an operating system for enterprise intelligence. That shift starts with a blunt view: The bottleneck in enterprise AI isn't the model. Though Oracle supports open models, emerging agent MCP and A2A frameworks, and modern data formats such as vector search and Apache Iceberg, its core argument is more opinionated: The most secure and scalable approach to agentic AI runs inside the database. Despite rapid advances in large language models, many companies remain stuck in pilot mode. Early experiments show promise, but scaling those systems into production has proved far more difficult. The issue isn't generating outputs. It's grounding those outputs in real, governed, constantly changing enterprise data. Most corporate data environments are fragmented by design. Information is spread across transactional systems, analytics platforms and data lakes, often duplicated and inconsistently governed. Layer AI agents on top -- systems designed not just to respond, but to act -- and those inconsistencies quickly become liabilities. Oracle's answer is to remove the seams. The company's latest push embeds agentic AI capabilities directly into the database, collapsing what has become an increasingly complex and costly stack. In a typical modern architecture, vector databases, orchestration frameworks and application logic sit alongside traditional systems, all requiring synchronization. Oracle is betting that this modular approach, while flexible, is ultimately too fragile, too costly and too exposed for production-scale, enterprise-grade AI. Instead, it is promoting a converged data engine -- one architecture where transactional data, embeddings, graph relationships, spatial data and security controls coexist and operate in real time. Central to that vision is a unified memory layer. Rather than moving data between specialized systems, the idea is to allow AI agents to operate directly on live enterprise data in its native form. If successful, this would reduce latency and eliminate the inconsistencies that arise from maintaining multiple copies of the same information. The company is also introducing what amounts to an internalized agent development model -- one that brings the creation and execution of AI agents inside the enterprise boundary. Today, much of that innovation is happening in external ecosystems, where flexibility comes at the cost of control. Oracle's approach is more restrictive, but also more governed, positioning agents as managed workloads rather than experimental tools. Enterprises aren't looking for experiments. They're looking for production. Security, long a selling point for Oracle, is being extended to this new paradigm. In traditional systems, access controls are often enforced at the application layer. Oracle is pushing those controls down into the database itself, applying policies at the row, column and cell level, and tying them to both user and agent identities. In a world where queries are dynamically generated, agentic AI guardrails are a shift that could prove to be a significant differentiator for enterprise workloads. Taken together, these moves reflect a broader strategy: Reduce AI data fragmentation. The current AI landscape is defined by specialization. A growing ecosystem of vendors offers solutions for every layer of the stack. That has accelerated innovation, but it has also introduced complexity -- and with it, operational risk. As organizations move beyond experimentation, stitching these components together becomes a challenge in its own right. Moving data between single-purpose systems adds latency and cost. Having agents make multiple stops to retrieve answers compounds the problem, and managing context across fragmented systems becomes unnecessary overhead. Oracle is betting that, for large enterprises, simplicity will outweigh modularity when it comes to agentic AI. At the same time, a converged data architecture must compete with a fast-moving ecosystem of specialized tools, each evolving rapidly. Developers, who have driven much of the momentum in AI, may resist more opinionated platforms. And many enterprises are already deeply integrated with hyperscale cloud providers, raising questions about how Oracle's approach fits alongside existing investments. Oracle's counter is pragmatic. Its agentic AI capabilities are designed to run across major cloud environments, including Amazon Web Services, Microsoft Azure and Google Cloud, allowing enterprises to activate AI where their data already resides. The goal is to minimize movement, reduce fragmentation and align with existing data gravity rather than disrupt it. Oracle is architecting AI around its customers' data. By bringing AI to where data already lives, organizations -- especially more conservative ones -- can activate AI within environments they already trust, turning AI into an extension of existing systems rather than an experimental overlay. The industry, in effect, is splitting along philosophical lines. One camp favors composability, where loosely coupled systems can be mixed and matched. The other, increasingly represented by Oracle, is advocating for convergence -- tightly integrated platforms designed to reduce operational friction. Both approaches have merit. The outcome will likely depend less on ideology than on execution. What is clear is that the center of the AI conversation is shifting. As enterprises move from prototypes to production, the challenges become less about generating content and more about managing data -- ensuring accuracy, consistency and security at scale. In that environment, the infrastructure layer regains importance. Oracle's strategy is to elevate the database from infrastructure to the control plane for agentic AI. If AI agents are to become embedded in core business processes, the systems that feed them data -- and govern their actions -- will matter as much as the intelligence they exhibit. Oracle is betting that the future of enterprise AI will be decided not at the edge of the stack, but at the data foundation itself. Exclusive Oracle World Tour video TheCUBE interviewed Tirthankar Lahiri, senior vice president of mission-critical and AI engines in Oracle's AI Database group:

Oracle's agentic Fusion play - from system of record to system of outcomes

Today at AI World London, Oracle announced twenty-two new Fusion Agentic Applications and significantly expanded AI Agent Studio. Steve Miranda, Executive Vice President of Applications Development, said that the goal is to move enterprise software beyond "passive systems of record" toward applications that can reason, decide, and act in pursuit of defined business objectives. In its own words, Oracle is shifting Fusion from a system of record to a system of outcomes. For buyers, this is well beyond the capabilities of copilots and generative AI assistants. Those tools may have provided a marginal uplift in productivity (and even that is debatable), but this suite of agentic applications essentially changes how work gets done. The speed at which this has moved is worth acknowledging. Eighteen months ago, Oracle was embedding large language models to generate text across Fusion - item descriptions, report summaries, etc. Useful, but limited and incremental. Then came individual agents, handling discrete steps in a process. Describing those agents, Miranda said: Then we moved to agents. We now have hundreds of agents across the applications. The way to think about it is that each agent is a small piece of AI that handles one step in a process. Take a recruiting process - you shortlist candidates, schedule interviews, do background checks, do onboarding. Each of those steps has an agent. Today's announcement is the step beyond that. Not individual agents handling individual tasks, but coordinated teams of agents working toward a business objective. And it's available now across the full Fusion portfolio - ERP, Supply Chain and Manufacturing, HCM, and Customer Experience. On the scale of the change this could bring, Oracle CEO Mike Sicilia compared it to aviation's shift to jet engines. On stage during his keynote, he said: I like to think about aviation's shift from propellers to jet engines as a comparison. When that happened, airlines didn't just change their routes - they were still moving people and goods - but what changed was capability. Jet engines expanded what was possible: faster routes, longer distances, entirely new markets, because the underlying technology could finally keep up with rising expectations. And that's exactly what AI is doing, and will do, for the enterprise. The twenty-two new Fusion Agentic Applications cover use cases that include:: a Collectors Workspace aimed at reducing days sales outstanding and accelerating cash collection; a Workforce Operations app targeting payroll errors and scheduling delays; a Design-to-Source Workspace for supply chain sourcing; a Cross-Sell Program Workspace for sales teams chasing expansion revenue. This means that rather than having users operating individual screens, entering data into predefined fields, telling the system what to record - the approach to ERP that's barely changed in thirty years - these applications accept a business objective and work toward it. Miranda used a good analogy: "Think back a couple of years ago...if you were going on vacation, you'd go into Google, search for hotels, search for available flights, search for things to do, restaurants, and so on. You'd get some answers, and then manually book airline tickets, the hotel, restaurants, tours. With an agentic framework, instead of asking what's a good hotel or what are the tourist attractions, you just ask: what should I do in London? And it gives you a fully mapped-out itinerary. A simple example, but that's the shift Miranda and Oracle are laying out for enterprise. From asking questions and doing the typing yourself, to giving objectives and having the system do the work. In a pre-brief ahead of the event in London, Natalia Rachelson, who leads Fusion Applications product management, said: The enterprise systems of the last 30 to 40 years recorded what happened, reported on what happened, and we all made decisions and moved business forward outside of the system. With agentic applications, it's very much a system that can make decisions autonomously, and act and execute." I pushed hard on governance with the Oracle team this week, given that this is the primary concern amongst CIOs and digital leaders in the diginomica network. In January, we ran a micro-pulse survey with 124 CIOs asking about their AI implementation experiences in 2025. Governance and risk management came out as the highest urgency and highest impact theme of the entire survey. One CIO described their experience as "overall quite challenging and at times frustrating - much of this stems from the organisation still defining AI governance and operating models, which has resulted in complex, unclear processes that are difficult to navigate." Another flagged the agent-specific tension, stating that they "worry about over-dependence on one LLM and have raised the company risk level as we become more dependent on agents". These aren't challenges specific to Oracle, but they provide context for the selling environment Oracle is stepping into. Oracle's response is that Fusion Agentic Applications inherit the entire governance model of Fusion itself. Rachelson outlined why this matters architecturally: The governance comes from the broader Fusion system of record. These applications inherit the user's role-based access controls, the security associated with that user, the approval hierarchy, all the rules and policies stored in the system. So if I can only see a subset of HR data, my agentic application can also only see that subset. The common critique of bolted-on AI in enterprise contexts is that it creates a new governance layer sitting outside the core system - new risks, new audit requirements, new conversations with regulators and workers' councils. Oracle's argument is that because its agents run inside Fusion, with native access to data, policies, and approval structures, that problem is largely avoided. I also asked specifically about audit trails - what does traceability actually look like if something goes wrong? Rachelson's answer was more substantive than I expected: Every single step is recorded - not just cases where an agent gets stuck and calls for human intervention, but every decision, why it was made, and how. Customers get a complete and full audit trail. They can test against it and review it, and that's often how they get comfortable with the system - seeing traceability and auditability in action. I also asked about automation and the level of autonomy these agents have. Oracle is taking a measured position - customers set their preferred level on a spectrum, most will start somewhere in the middle and increase over time (towards full autonomy) as confidence grows. Another point that I put to Miranda was around how organizations are successfully - if at all - changing operating models towards agentic AI architectures. The genuinely disruptive thing about these agents is that they don't care about organizational boundaries. They cross departments, functions, and reporting lines to accomplish a goal. Which is precisely why they're powerful - and why they create a real problem for the humans who have to reorganize around them. This is what the CIOs in our network raise consistently as their primary concern. The technology is becoming easier to understand, but changing operating models to really reap the rewards feels like a complex and challenging job. I asked Miranda: are you seeing examples yet of how to effectively bring that organisational change about? He said: I can't point to concrete examples of success just yet, but I can say there's enormous pressure across the ecosystem. Think about what I described in terms of business process flows: there are going to be certain tasks that AI does extraordinarily well. If today three out of five of your tasks fall into that category, you probably won't be doing those much longer. But you'll still have the other two. What does that mean? Your peer sitting next to you might have ten tasks and AI only handles three of theirs. He went further on the cross-functional dimension: We see that changing fundamentally too. The agentic applications we're building are all role-based, at a higher role level, and what happens underneath will change fundamentally. People are trying to figure out what's being automated and what they need to do as a result. It is reforming the structure of teams and roles. That's the work that sits ahead of every enterprise considering this. And it's the work that no vendor can do for you. A couple of other points worth highlighting, even if they might be obvious to some, are around how Oracle tests its agents across multiple models and picks the best for each task, with no commitment to any single provider. The infrastructure position is that because the major LLMs are available on OCI, customers will always get the benefit of whatever the best option turns out to be. On pricing, agentic AI is included within existing Fusion subscriptions, with a consumption model - measured in action units - for usage beyond the base allotment. Miranda said that user-based pricing will eventually give way to something transaction or company-size based: There will come a day when our pricing model for our base subscription shifts from user-based to some sort of transaction-based or company-size-based metric. Nobody is in business to run ERP - they have to run ERP. The more we can save them on the ERP side, the more they can invest in what they're actually in business to do. The system of record to system of outcomes framing is the right way to think about what Oracle is attempting here. The architectural argument - agents living inside Fusion, inheriting its governance model, meaning enterprises can move faster without creating new risk - is coherent. If it holds up in production, it will be impressive. However, I keep coming back to the enterprise reality of it all. This is so far ahead of where so many organizations are, as it really does require buyers to think through how they operate (which could be quite drastically different). Teams, departmental lines, job roles - these agentic architectures impact them all. That's not totally Oracle's job to have the answer. Everyone is thinking through the use cases and implications, but I really do urge CIOs (or anyone in charge of this) to recognize this isn't a straightforward tech rollout. This is not like for like. We are all still figuring out the playbook.

Oracle Fusion agentic applications signal shift toward autonomous enterprise software - SiliconANGLE

Oracle Fusion agentic applications signal shift toward autonomous enterprise software Oracle Corp. is imbuing its Fusion applications with agentic artificial intelligence in a move aimed at shifting operational software from an assistive role into direct execution of business processes. The company today unveiled Fusion Agentic Applications, a set of AI-powered applications embedded within its Oracle Fusion Cloud suite. They're designed to reason, make decisions and take action across finance, human resources, supply chain and customer experience workflows. The announcement reflects a broader transition in enterprise software that is seeing vendors attempt to evolve from systems of record into systems of execution. Oracle executives said the move builds on earlier investments in copilots and AI assistants but represents a more fundamental architectural change. "We believe that AI is, especially for enterprise organizations, moving from advisers and copilots to being able to execute work," said Chris Leone, executive vice president of applications development at Oracle. Fusion Agentic Applications are built around coordinated groups of specialized AI agents that collaborate to achieve specific business objectives. Rather than responding to prompts or generating reports, they are designed to continuously improve outcomes by orchestrating tasks, evaluating tradeoffs and recommending or executing actions. Leone compared the model to assembling a team of specialists to solve a problem. "Each agent has a specialty and the overall agentic application has a particular outcome that it's trying to achieve," he said. The applications operate natively within Oracle's transactional systems, giving them access to enterprise data, workflows, policies and approval hierarchies. Oracle said this tight integration allows agents to executive sophisticated workflows while maintaining governance and auditability. Oracle is initially launching 22 agentic applications targeting use cases that span multiple business functions including workforce scheduling, supplier sourcing, cross-sell program management and cash collection. Leone said the first set of use cases addresses what he described as high "cognitive load," where work is often fragmented across systems or handled manually. In the case of workforce management, "The agent can reason over all the information, understand context and make recommendations as to specifically which absences and schedules to approve," he said, The agents are designed to operate with varying levels of autonomy. In a "human in the loop" mode, agents generate recommendations for approval by users. Organizations can also allow certain actions to be executed automatically, with the ability to increase autonomy over time. "Customers can turn the dial up for more automation," Leone said. A core component of the new features strategy is Oracle AI Agent Studio, which has been updated with a new Agentic Applications Builder. The tool allows customers to create their own agent-driven applications using natural language, assemble teams of agents and connect them to enterprise data and workflows. The platform also includes capabilities for workflow orchestration, contextual memory, content intelligence and performance monitoring, including dashboards to measure the return on investment of AI-driven processes. Oracle said the system supports integration with external platforms through application programming interfaces and the open-source Agent2Agent protocol developed by Google LLC. "We can send a query or question to another agent [in an external application] and get a response back," Leone said. Agents inherit role-based access controls and data permissions from existing application security frameworks in Fusion Applications. Oracle has also implemented auditability features to track changes to agent behavior and ensure accountability. "If someone changes a system prompt we can track that," Leone said. In early testing, organizations have reported time savings of up to 40% to 50% in support scenarios, he said. Oracle is adopting a hybrid pricing model for the new capabilities. Basic agents using built-in models are included with existing applications at no additional charge. More advanced capabilities powered by premium large language models will incur usage-based charges. The company also plans to expand the ecosystem by enabling partners to build and distribute their own agentic applications, positioning the platform as a foundation for broader enterprise automation. Leone said the shift toward agent-driven execution is likely to become standard across the industry. "We believe all systems of record will have to become systems of execution," he said.

Oracle Fusion Cloud Gains New Agentic AI Capabilities - Oracle (NYSE:ORCL)

The move is also coming with S&P 500 futures slightly lower, setting up a mixed tape heading into the open. Fusion Agentic Application Launch Oracle is launching Fusion Agentic Applications, describing them as a new class of enterprise apps that run on Oracle Cloud Infrastructure and are powered by "industry-leading LLMs." The company said 22 new Fusion Agentic Applications are now available, targeting specific objectives for leaders across finance, HR, supply chain, and customer experience. Oracle framed the release around helping organizations "unlock time, capacity, and outcomes that were previously out of reach," positioning the tools as an extension of its Fusion Cloud Applications suite. AI Product Expansion Also, Oracle is expanding its AI Agent Studio for Fusion Applications with an "Agentic Applications Builder" plus new intelligent workflow tools. The company has positioned the update as a way for customers to scale "outcome-driven AI" and measure value. The company also said the tools are available at no additional cost and highlighted a partner ecosystem of 63,000-plus certified experts trained in Oracle AI Agent Studio. Oracle also pointed to added capabilities inside the studio, such as orchestration, advanced testing, validation, and built-in security to help customers and partners create and manage AI agents and agentic applications. AI Database Innovations Also, the company rolled out agentic innovations for its AI Database, leaning into the "AI-era" security and data-governance conversation. Oracle's announcement centers on "Oracle AI Database," adding new "agentic AI capabilities designed for business data," with the company positioning the update as a way to "eliminate the need to build and maintain data-movement pipelines" that can add complexity and security risk. On the security side, Oracle says Oracle AI Database is built to help customers "safeguard data from external attacks, insider misuse, accidental disclosure, and unintended exposure to LLMs" across "multicloud, hybrid, and on-premises environments." Oracle Analyst Outlook The stock carries a Buy Rating with an average price target of $252.92. Recent analyst moves include: Mizuho: Outperform (Lowers Target to $320.00) (Mar. 16) Guggenheim: Buy (Maintains Target to $400.00) (Mar. 13) Citigroup: Buy (Raises Target to $320.00) (Mar. 12) Top ETF Exposure Stock Price Activity: Oracle shares were up 0.43% at $155.00 during premarket trading on Tuesday, according to Benzinga Pro data. Photo via Shutterstock This content was partially produced with the help of AI tools and was reviewed and published by Benzinga editors. Market News and Data brought to you by Benzinga APIs To add Benzinga News as your preferred source on Google, click here.

Oracle Launches Tools to Help Enterprises Deploy AI Agents | PYMNTS.com

The company's new Fusion Agentic Applications are built into Oracle Fusion Cloud Applications and are powered by teams of specialized AI agents, according to a Tuesday (March 24) press release. Twenty-two new Fusion Agentic Applications are available, including ones designed to assist human resources (HR) leaders, supply chain leaders, sales teams and finance teams, according to the release. "With Fusion Agentic Applications, we are moving enterprise software beyond passive systems of record and providing our customers with applications that can reason, decide and act in pursuit of defined business objectives," Steve Miranda, executive vice president of applications development at Oracle, said in the release. Oracle announced in another Tuesday press release that it updated its Oracle AI Agent Studio to add a new agentic applications builder and new capabilities that support workflow orchestration, content intelligence, contextual memory and return on investment (ROI) measurement. Oracle AI Agent Studio is the company's development platform for AI automation and agentic applications. "Builders can create AI automations and agentic applications using natural language that are powered by enterprise AI agents capable of reasoning, taking actions across business systems and continuously executing processes," Chris Leone, executive vice president of applications development at Oracle, said in the release. A third new offering introduced Tuesday by Oracle is new agentic AI innovations for Oracle AI Database, which architects agentic AI and data together across operational databases and analytic lakehouses. This product enables AI agents to securely access real-time enterprise data and use it with large language models to provide business insights, according to a Tuesday press release. "With Oracle AI Database, customers don't just store data, they activate it for AI," Juan Loaiza, executive vice president, Oracle Database Technologies, said in the release. PYMNTS reported in October 2025 that Oracle is rebuilding the data foundation for AI by merging data governance, analytics and AI in one environment. The company's architecture allows enterprises to run AI workloads without moving sensitive data into external stores. During the same month, Oracle brought embedded AI agents and a new AI Agent Marketplace into its Fusion Cloud Applications and Industry Applications. More than 600 embedded agents were available at that time.

Oracle Fusion's New Agentic AI: Automating the Future of Enterprise Work

A new class of enterprise applications reinvents how work works by helping organizations unlock time, capacity, and outcomes that were previously out of reach Oracle today announced Fusion Agentic Applications, a new class of enterprise applications powered by coordinated teams of specialized AI agents that are outcome-driven, proactive and reasoning based, and engineered for enterprise execution. Built into Oracle Fusion Cloud Applications, Fusion Agentic Applications can make and execute decisions within business processes by securely accessing unified enterprise data, workflows, policies, approval hierarchies, permissions, and transactional context. Unlike copilots, AI assistants, or other AI add-ons, being native to the transactional system enables Fusion Agentic Applications to execute in real time, at enterprise scale, with full governance. "The way work gets done no longer matches the speed, complexity, or expectations of modern business as too much time is spent managing processes instead of driving outcomes," said Steve Miranda, executive vice president of Applications Development, Oracle. "With Fusion Agentic Applications, we are moving enterprise software beyond passive systems of record and providing our customers with applications that can reason, decide, and act in pursuit of defined business objectives. This is a huge step forward for the industry and will help our customers achieve faster outcomes, focus their valuable time on strategic activities, and redefine how work works." Oracle's Fusion Agentic Applications transform traditional business software from a simple system of record into a dynamic system of outcomes. By deploying specialized teams of AI agents with defined roles and decision authority, these applications autonomously determine the optimal path to achieve high-level business objectives. Operating strictly within the existing Oracle Fusion security framework, these agents progress routine actions within established guardrails, surfacing only the critical exceptions or nuanced trade-offs that require human judgment. This approach ensures that work moves forward with coordinated reasoning and execution, allowing organizations to bridge the gap between static data entry and active, objective-driven results. To maintain this momentum, the platform leverages persistent shared context and continuous reasoning to navigate complex, multi-step workflows without losing sight of prior decisions or intent. Unlike traditional automation that stops after a single task, these agentic applications constantly re-evaluate changing conditions and nuances to keep processes in motion. This sophisticated level of autonomy is balanced by enterprise-grade governance, featuring role-based access and end-to-end traceability. By providing full auditability and a transparent execution path for every AI-powered action, Oracle ensures that while the work is automated, the decision-making remains fully accountable and aligned with corporate standards. Powered by industry-leading LLMs and hosted on Oracle Cloud Infrastructure, the new suite of 22 Fusion Agentic Applications extends the world's most comprehensive cloud portfolio to drive superior business outcomes. By shifting from reactive manual tasks to proactive, intelligent operations, these applications empower leaders across finance, HR, and supply chain to hit specific strategic targets. For example, the Workforce Operations agent streamlines payroll and scheduling to reduce errors, while the Design-to-Source Workspace agent unifies engineering and sourcing decisions to slash product costs and compliance risks. This evolution toward "always-on" enterprise intelligence further transforms revenue and cash flow management. The Cross-Sell Program Workspace agent helps sales teams boost win rates by identifying growth opportunities automatically, lowering acquisition costs in the process. Simultaneously, the Collectors Workspace agent optimizes working capital for finance teams by accelerating cash collection and improving "promise to pay" conversions. Together, these innovations ensure that complex business processes remain continuous, coordinated, and focused on high-value results. The new Fusion Agentic Applications are supported by a full AI ecosystem anchored by Oracle AI Agent Studio. With the new Agentic Applications Builder in the Oracle AI Agent Studio, organizations can build, connect, and run AI automation and agentic applications using reusable Oracle, partner, and external agents without traditional application development. In addition, built-in observability, ROI measurement, and safety controls enable agents to deliver measurable value and operate responsibly at scale. The introduction of Oracle Fusion Agentic Applications marks a pivotal transition in enterprise software, moving beyond simple task automation toward true outcome-driven execution. According to Mark Smith of ISG, this shift is a critical step on the journey toward the autonomous enterprise. By providing a platform that can coordinate agents across various business functions while maintaining strict security and approval protocols within the application suite, Oracle offers a significant differentiator for organizations looking to scale automation effectively. From an operational perspective, the power of these agentic applications lies in their ability to eliminate the "noise" of routine coordination that typically consumes employee time. Kevin Permenter of IDC notes that by allowing AI agents to handle repetitive follow-ups and administrative tasks, human workers can focus exclusively on high-level exceptions that require professional judgment. This shift not only helps organizations reclaim valuable time but also enhances operational consistency and accelerates decision-making across finance, HR, supply chain, and customer experience departments. Furthermore, Oracle's architectural strategy addresses a persistent challenge in the industry: the tendency to "bolt on" AI to existing workflows without deep integration. Michael Fauscette of Arion Research highlights that because these agents operate natively within the Fusion suite, they have direct access to essential data, policies, and governance frameworks. This deep integration allows enterprises to move swiftly from the experimentation phase to full operational execution, leveraging a system that is inherently designed for the complexities of modern business transactions.

Oracle Reworks Fusion Finance and Procurement Apps for AI Agents

Oracle Revamps Fusion Software to Let AI Agents Manage Finance and Procurement Tasks Oracle is updating its cloud finance and procurement software so companies can use AI agents inside routine business processes. The move is part of a wider shift in enterprise software, where vendors are adapting products for AI tools that can complete tasks on behalf of users. Oracle plans to announce the changes at an event in London on Tuesday local time. The update applies to Oracle Fusion, which supports finance, supply chain, human resources, and . The firm said users will be able to ask business questions in simple language, while AI finds data across Oracle systems and connected third-party software. The company added that the software will keep operating within existing controls, including permissions, approvals, and policy rules.

Oracle reworks its suite of cloud software as 'agentic apps'

(In March 23 story, corrects headline and paragraphs 1, 4 and 6 to reflect that Oracle introduced agentic apps that work across its software suite, rather than individual AI agents) SAN FRANCISCO, March 23 (Reuters) - Oracle is revamping its cloud-based software suite used by large companies to work with artificial intelligence agents as "agentic apps," with a goal of having humans ask the system for business outcomes. The changes, which Oracle planned to announce at an event in London on Tuesday local time, are part of a broader trend in which providers of highly specialized corporate software are revamping it to be used by AI agents that can carry out tasks on behalf of human users. Oracle's shares are down about 40% this year as the company has been swept by investor concerns that AI tools will largely supplant complicated business software. Oracle's executives have argued that the company is embracing AI tools to keep its software ahead of those changes. In the latest case, Oracle is updating its Fusion suite of software, which includes core business tasks such as planning production in factories and collecting money from customers, among other functions such as human resources. Steve Miranda, executive vice president of applications development at Oracle, said the company's goal is to make it easier to focus on business questions, such as how to make a new product design cheaper and faster, while minimizing the risks of supply chain disruptions. The data needed for those decisions, Miranda said, is scattered among the various applications in Oracle's suite and third-party software connected to it. Teams of AI agents will take on tasks such as entering and gathering data and making recommendations needed to reach business outcomes, while for human employees there will be more emphasis on skills like knowing how to negotiate with suppliers and what kind of risk tolerance for supply disruption a company has, Miranda said. "Typing in an invoice isn't a particularly high-value skill to your enterprise or to the person you know who does that part of their job," Miranda said. "Decision making is still kind of up to that human and weighing the different pros and cons of that case. But certainly the execution, the typing of the invoices, the typing of the purchase order, that is what is going to be replaced in whole in AI." (Reporting by Stephen Nellis in San Francisco; Editing by Bill Berkrot and Matthew Lewis)

Oracle reworks its finance, procurement apps for AI agents

SAN FRANCISCO, March 23 (Reuters) - Oracle is revamping its cloud-based financial software used by large companies to work with artificial intelligence agents, with a goal of having humans ask the system business questions and letting AI figure out how to find the data. The changes, which Oracle planned to announce at an event in London on Tuesday local time, are part of a broader trend in which providers of highly specialized corporate software are revamping it to be used by AI agents that can carry out tasks on behalf of human users. Oracle's shares are down about 40% this year as the company has been swept by investor concerns that AI tools will largely supplant complicated business software. Oracle's executives have argued that the company is embracing AI tools to keep its software ahead of those changes. In the latest case, Oracle is updating its Fusion suite of software, which includes core business tasks such as planning production in factories and collecting money from customers. Steve Miranda, executive vice president of applications development at Oracle, said the company's goal is to make it easier to focus on business questions, such as how to make a new product design cheaper and faster, while minimizing the risks to supply chain disruptions. The data needed for those decisions, Miranda said, is scattered among the various applications in Oracle's suite and third-party software connected to it. AI will take on tasks such as entering and gathering data and making recommendations, while for human employees there will be more emphasis on skills like knowing how to negotiate with suppliers and what kind of risk tolerance for supply disruption a company has, Miranda said. "Typing in an invoice isn't a particularly high-value skill to your enterprise or to the person you know who does that part of their job," Miranda said. "Decision making is still kind of up to that human and weighing the different pros and cons of that case. But certainly the execution, the typing of the invoices, the typing of the purchase order, that is what is going to be replaced in whole in AI." (Reporting by Stephen Nellis in San Francisco; Editing by Bill Berkrot)