Oracle Just Launched 22 AI Agents. Most Companies Aren't Ready

On March 24, 2026, at the Oracle AI World conference in London, Oracle made a major move: the release of 22 Fusion Agentic Applications. These represent a new category of enterprise software where teams of specialized AI agents collaborate within business workflows to reason, make decisions, and execute tasks autonomously.

Steve Miranda, Executive Vice President of Applications Development at Oracle, put it plainly: organizations spend far too much time managing processes instead of driving outcomes.

That same day, cybersecurity firm Nudge Security released its latest AI agent discovery tool alongside a sobering statistic: 80% of enterprises face data access risks from employee-built AI agents. Staff are assembling and deploying agents across platforms like Copilot Studio, LangChain, and CrewAI without IT or security oversight.

On one side, enterprise software vendors are pushing hard toward agent-native applications. On the other, unmanaged "shadow AI agents" are spreading rapidly across internal workflows. The convergence of these two trends raises an unavoidable question: who actually controls AI inside your organization?

From "Looking Up Data" to "Making Decisions": Oracle's Paradigm Shift

The core message behind Oracle's announcement is straightforward: enterprise software is shifting from a "System of Record" to a "System of Outcomes."

For decades, ERP, HCM, and CRM platforms served primarily to record data: financial ledgers, employee records, and customer touchpoints. Later, vendors layered on copilots to offer suggestions. Oracle's Fusion Agentic Applications take a different approach. Each application consists of specialized AI agents with defined roles and decision-making authority. Instead of waiting for prompts, they actively drive operational goals.

Oracle introduced 22 applications across finance, human resources, supply chain, and customer experience:

• Human Resources: A Workforce Operations agent application automates payroll error reduction and accelerates shift approval workflows.
• Finance: Dedicated agents accelerate accounts receivable collections and lower days sales outstanding (DSO).
• Supply Chain: Agents coordinate supplier sourcing and execute procurement operations.

Mark Smith, Chief AI and Software Analyst at ISG Research, noted that Oracle's Fusion Agentic Applications mark a meaningful shift for enterprise software, moving beyond task automation into outcome-driven execution.

Alongside these applications, Oracle updated its AI Agent Studio with an Agentic Applications Builder. The tool lets organizations assemble custom multi-agent teams using natural language, connecting them directly to enterprise workflows. Oracle also announced support for Google's open-source Agent2Agent protocol to enable cross-platform communication.

Critically, Oracle is including these capabilities at no additional cost for Fusion Applications customers. The strategic bet is clear: whoever gets enterprises accustomed to agent-driven execution will secure the primary entry point for next-generation business software.

The Flip Side: Shadow AI Agents Are Bypassing Security Controls

While Oracle paints a picture of a governed, agent-driven workplace, day-to-day enterprise realities look very different.

Nudge Security's findings highlight that employees are building agents on their own, often leaving security teams with zero visibility. These shadow AI agents connect to corporate SaaS platforms using OAuth tokens, API keys, or webhooks without going through formal security reviews.

Unlike traditional shadow IT—where an employee might simply use an unauthorized SaaS tool—shadow AI agents act as autonomous, non-human identities. They can query databases, invoke APIs, trigger workflows, and generate or publish content on their own.

Earlier research from Noma Security identified similar risks. Shadow AI agents are harder to catch than conventional shadow IT because they often run in lightweight containers or transient processes. They spin up, execute a series of tasks, and terminate before legacy vulnerability scanners can detect them.

This creates a paradox: companies are spending heavily on official agent deployments while remaining blind to the rogue agents already running across their networks.

Big Tech Moves Past the "Copilot" Era

Oracle is far from alone. Over the past several weeks, enterprise software providers have accelerated their shift toward autonomous agents:

• Salesforce reported that Agentforce annual recurring revenue crossed $800 million and partnered with NVIDIA to offer on-premise agents for regulated industries.
• Meta detailed its internal Ranking Engineer Agent (REA), an autonomous system capable of running end-to-end machine learning experiments for weeks at a time. In its first production deployment, REA doubled model accuracy improvements and multiplied engineering output by five.
• The U.S. Department of the Treasury launched its AI Innovation Series, focused specifically on bringing enterprise AI into production within financial services.

Vendors have largely stopped relying on terms like "copilot" or "assistant." Oracle explicitly separates its agentic applications from add-on assistants. Salesforce and NVIDIA consistently favor "agentic" over "assistive." The industry consensus is settling: enterprise AI is shifting from an advisory role to an operational one.

The Starbox Perspective: Execution Demands Structured Knowledge

Oracle's release reinforces an operational truth we see every day at Starbox AI: AI agents cannot execute effectively unless an organization's knowledge, workflows, and governance rules are already structured.

Oracle can offer autonomous execution because Fusion already houses core enterprise data, approval hierarchies, access permissions, and business rules. Agents do not work in a vacuum. They require a clear structural framework to understand the context of the business they operate in.

This reality shapes how we approach enterprise deployment. Starbox AI uses a three-tier architecture (Organization → BOX → Agent) alongside a four-dimensional knowledge framework that integrates pre-trained capabilities, industry context, company-specific IP, and real-time operational data. This structure serves as the necessary bridge between raw enterprise records and autonomous execution. Without it, an AI agent functions like a capable new hire who has never been onboarded: high potential, but zero operational awareness.

At the same time, the rise of shadow AI agents reveals a clear operational gap. Employees build their own agents because standard IT processes move too slowly to address front-line demands. It is not an issue of employee discipline; it is an issue of internal supply. Providing access to over 300 production-ready agents and delivering working deployments within two to four weeks allows enterprises to close that gap quickly, replacing unmanaged tools with sanctioned infrastructure.

Oracle's rollout of 22 agentic applications marks an important milestone. Enterprise AI is no longer just offering advice; it is taking action.

Before you hand off decisions to autonomous software, ask yourself: is your operational data organized well enough to support that execution safely? Without structured knowledge and clear governance, faster agent adoption simply increases your operational risk. Build the foundation first—then turn on execution.