Summary Bullets:

• Oracle shifts pitch from technology to outcomes, driven by AI-injected tools.

• Oracle APEXLang, slated for 2026, to modernize Oracle’s development practices.

Last week’s Oracle AI World couldn’t have been timelier, attended by customers and partners still buzzing from a corporate earnings report, which triggered the tech giant’s stock to soar based on its mounting investments in AI and cloud infrastructure.

Executive Chairman and CTO Larry Ellison’s high-level keynote speech included profound possibilities and some examples of a variety of outcomes that AI advancements can have on global enterprises. Executives continued to carry his message in other keynotes, noting the company’s shift in focus from CIOs to CEOs through conversations that emphasized outcomes versus products. For example, in one case, Oracle’s ability to apply AI-injected applications, app platforms, and data platforms to a particular healthcare clinic resulted in productivity gains, which saved individual health workers 100 minutes per day.

He and other executives further described situations involving various medical diagnostic imaging and genetic testing with examples of how AI will diagnose, treat, and cure health issues at significantly greater levels and speeds through modernized code bases, medical systems, and ecosystems.

Oracle announced new functionality across Oracle Cloud Infrastructure’s (OCI) comprehensive cloud offering, spanning its developer portfolio: AI Agent Studio, Fusion AI Agent Marketplace, and Agent Hub (preview) AI tools for business users. Oracle’s AI agent studio has been enhanced to build and deploy AI agents across the enterprise including Oracle Fusion Applications. Oracle’s new AI agent marketplace extends the company’s LLM ecosystem and third-party agent-building resources. Ellison noted that Oracle’s low-code and automation technology, Oracle Application Express (APEX), will continue to demonstrate a growing role in AI code generation of applications connected through workflows and shored up by security. Advancements will make applications developed more scalable and reliable. This led to other application development discussions throughout the week, including some on the future of Oracle APEX.

A little-known app development tool, Oracle APEXLang, shows promise in modernizing and extending Oracle’s current development practices. Set for 2026 release, the Apex extension uses a structured, file-based format to build and format Oracle APEX applications, specifically to enable app development to be integrated with enterprises’ latest digitization practices.

Oracle APEX, traditionally used in a browser-based, declarative environment over the past five years, is valued by enterprise developers for its low-code cloud service advantage, traditionally used to build apps on Oracle databases. Oracle APEXLang represents a significant shift for these traditional developers. Features include version control support (e.g., Git) and tools to adhere to CICD pipelines for improved automated test and deployment. It works with developers’ app platforms of choice including code assistants, because the new file-based approach is particularly well suited for GenAI and LLMs. Oracle research notes Oracle APEXLang is not a replacement for Oracle APEX, SQL, or JavaScript, but an enabler for defining components of applications.

Summary Bullets:

• Agentic AI streamlines workflow automation and transformations.
• Application and automation platforms to integrate agentic AI capabilities in next 12 months.

Digital transformations will receive a major boost over the next 12 months following new platform integrations with agentic AI. The AI-injected solutions will significantly streamline the creation of workflow automation, which are critical to organizations moving to migrate legacy apps to cloud environments in order to realize CICD and improved application lifecycle efficiencies.

This next generation of intelligent automation will have far-reaching ramifications among service providers, from traditional PaaS players to leading automation vendors to newer telco/infrastructure providers offering managed Kubernetes services.

Business transformations have been largely stalled over the past few years due to the fact that many enterprises lack the internal expertise necessary to configure the backend integration and connectivity to enable workflows that support critical business processes. Automation leaders – including Automation Anywhere, UiPath, SS&C Blue Prism, ServiceNow, and Pegasystems – have played a pivotal role in advancing workflow automation, particularly predictable and rules-based workflows. Application platform solutions including Microsoft Power Platform and IBM Cloud Pak for Business Automation also compete in this space.

In coming months, these solutions and platforms will be equipped with advanced cognitive capabilities such as generative AI (GenAI) and agentic AI to enable dynamic business processes capable of adapting and reasoning in an autonomous fashion. This will be a welcome relief to those enterprise personas involved in back-office transactional processing where accuracy and quality of solutions are critical. They will be most inclined to rely on their trusted technology partners integrating such agentic capabilities through mature platform services.

Automation and platform leaders are only just beginning to offer the industry glimpses into their agentic AI roadmaps, having spent the last couple of years integrating GenAI into developer tools and workflow solutions. Beta versions of AI agent capabilities are starting to appear, typically in the form of prebuilt templates and ultimately agent building toolkits and agent orchestration management capabilities.

GlobalData will be closely following the slew of later conferences hosted by platform providers including IBM, Oracle, Salesforce, AWS, and multi-vendor Kubernetes/DevOps show KubeCon for advancements in this space. Similarly, IT ops teams should keep an eye out for a constantly changing ecosystem of players and partnerships in this space, which will encourage more service providers to support global companies struggling with digitization integrations.

Summary Bullets:

• Spring/summer platform conferences led with AI agent news and strategies
• AI agents represent the leading innovation of app modernization, but DevOps should be wary of over-promising

During this season of cloud platform conferences, rivals are vying to own the headlines and do battle in the cloud wars through their latest campaigns and strategies involving AI agents.

2024’s spring/summer conferences led with GenAI innovations–2025’s with agentic AI. AI assistants and copilots have transformed into tools used to create customized agents, unleashing claims of new capabilities for streamlining integrations with workflows, speeding the application development lifecycle, and supporting multi-agent orchestration and management. Vendors are making bold promises based on agentic AI for its ability to eliminate a multitude of tasks mandated by humans and taking workflow automations to new heights.

AI agents, which can autonomously complete tasks on behalf of users leveraging data from sources external to the AI model, are accelerating the transition towards a more disruptive phase of GenAI. Enhanced memory capabilities enable the AI agents to develop a greater sense of context, including the capacity for “planning.” Agents can connect to other systems through APIs, taking actions rather than just returning information or generating content.

Recap of the latest AI agent events:

• Amazon announced Bedrock AgentCore, a set of DevOps tools and services to help developers design custom applications while easing the deployment and operation of enterprise-grade AI agents. The tools are complemented with new observability features found in AWS CloudWatch.
• Joining the Google Gemini family of products, including Gemini 2.5 and Pro, Vertex AI Agent, ADK, and Agentspace, is Google Veo 3, a GenAI model providing more accessibility to high quality video production.
• OpenAI released ChatGPT agent, an AI system infused with agentic capabilities, that can operate a computer, browse the web, write code, use a terminal, write reports, create images, edit spreadsheets, and create slides for users
• Anthropic released Claude Code, which uses agentic search to understand an entire codebase without manual context selection and is optimized for code understanding and generation with Claude Opus 4.
• IBM announced watsonx Orchestrate AI Agent, a suite of agent capabilities that include development tools to build agents on any framework, pre-built agents, and integration with platform partners including Oracle, AWS, Microsoft, and Salesforce.

Cloud platform providers are strategically highlighting their most salient strengths. These range from the breadth of their cloud stack offerings to mature serverless computing solutions to access to massive developer communities via popular Copilot tools and Marketplaces. Yet all are focused on gaining mind share amidst heated campaigns of not only traditional platform rivals, but an increasingly crowded ecosystem of new platform and digital services providers (in the form of infrastructure providers) vying to catch the enterprise developer’s attention.

Recent vendor announcements are aiming to strike a chord among over-taxed enterprise IT operations teams, with claims of easing operational provisioning complexities involved with moving modern apps into production. Use cases supporting these claims remain scarce, and details to help prove new streamlined and low-code methods, particular around AI agent orchestration, are still vague in some cases. Enterprises should remain vigilant in seeking out technology partners providing a deep understanding of an evolving technology which comes with a lot of promises.

AI innovations in the form of agents are promising new levels of automation that require little to no human interaction, targeting DevOps team members facing the arduous task of digital transformations. AI agents promise to transform the process of application development and to ease the burden of app and infrastructure modernization complexities.

Application platform providers are laser focused on enterprise developers with their first round of high-productivity agentic tools currently in release. AI assistants and copilots are transforming into tools that create customized agents, unleashing new capabilities for streamlining integrations with workflows, speed the application development lifecycle, and support multi-agent orchestration and management.

In 2025 organizations are pivoting from building deterministic workflows to implementing AI agents that learn, adapt, make decisions, and perform complex tasks using reasoning and longer-term memory. AI agents represent a new era of AI and GenAI because they act autonomously as independent agents, which can prove to be highly valuable among developers who work in fast-paced and dynamic software development environments. And while AI agents can autonomously complete tasks on behalf of users, agentic AI promises even greater automation to a range of complex tasks transforming business processes. They are designed to solve problems, attack tasks with minimal human input, and learn and become even more effective over time.

This unprecedented reliance on AI by a much broader audience of participants will heavily impact the market. GlobalData predicts that the overall AI market will see a 35% increase in 2025 over 2024, with a compound annual growth rate of 41% from 2023 to 2028. The agentic AI segment is expected to play a massive role in this growth.

Recent events have triggered an uptick in agentic activity including Microsoft’s autonomous agent enhancement to its popular Copilot AI assistant, which integrates with GitHub products, immediately targeting an audience of 15 million developers. Further, Google added agent development capabilities to its flagship Vertex AI portfolio of developer tools, since adding Agent Development Kit for building multi-agent systems, as well as introducing the new A2A protocol.

Rivals have joined the fray. Amazon Q Developer features agents that automate unit testing, documentation, and code reviews, accessed via AWS Management Console and offered through GitLab. Salesforce continues to evolve Agentforce through integrations with its developer platforms and business apps, to help organizations deploy autonomous AI agents across business functions such as customer support, sales, finance, and marketing.

Summary Bullets:

• Eight prominent LLM/GenAI competitors are featured in GlobalData’s new LLM Competitive Landscape Assessment. Google Gemini has been named Leader.

• In addition to core model capabilities, other factors are considered, including enterprise tools and partner ecosystems built around the GenAI model.

GlobalData has just released its first LLM Competitive Landscape Assessment, highlighting the strengths and challenges of eight heavy-hitters in this space.

The new report evaluates the competitors’ differentiators of core model technology including context windows, multimodal and multilingual capabilities, vertical and horizontal use cases, AI guardrails, ecosystem, professional services, and go-to-market strategies.

Within this report, Google has been named ‘Leader’ by GlobalData due to a combination of highly developed model capabilities in the Google Gemini family and sophisticated enterprise tooling to build and scale generative AI (GenAI) applications. OpenAI is ‘Very Strong’ thanks to its core model technology with solid code generation and multilingual capabilities, multimodality, and context window size. Microsoft is also rated ‘Very Strong’ for its high degree of penetration in the enterprise and tooling anchored in the powerful capabilities of its exclusive GenAI partner, OpenAI, and for Microsoft’s proprietary model, Phi. IBM is also rated in the ‘Very Strong’ category for its strengths in generating computer code, along with a broad range of native language support and third-party model support. Amazon, Anthropic, and Meta have been ranked ‘Strong,’ and Cohere is ranked ‘Competitive.’

GenAI platforms are largely based on multimodal foundation models and large language models (LLMs); these are borne out of growing interest in accessing natural language processing (NLP) to query computers, following the significant advancements in AI seen in recent years. These include machine learning and deep learning via neural networks, also called generative adversarial networks (GANs), and finally the emergence of the ‘transformer’ architecture in 2017, representing breakthrough efficiencies in training models.

The phenomenon of GenAI builds on the precursor of new software architectures, hybrid cloud, automation, and advancements in AI, resulting in the emergence of LLMs. LLMs are deep learning models trained using vast amounts of text. They are designed to produce new levels of content creation, automation of repetitive tasks, and deliver personalization for improving the customer experience, and their performance depends on the quality and size of the pretraining dataset.

The term LLM was largely unheard of before the end of 2022, which makes its explosive growth and mega investments both startling and somewhat overwhelming. Its immaturity also makes evaluating competitors’ closely guarded training methodologies challenging. The new comprehensive competitive landscape assessment also includes market drivers, buying criteria, and vendor and buyer recommendations, in addition to the eight product evaluations (please see Large Language Models (LLM): Competitive Landscape Assessment.

Following OpenAI’s release of ChatGPT, major cloud and platform providers, hardware/chip manufacturers, and startups moved quickly, recognizing the potential of a revolutionary technology unparalleled since the birth of the internet. Over the past 18 months, these vendors have leveraged their previous AI development efforts and set out building and training models as part of a GenAI portfolio to help address customers’ business transformations.