Oracle’s New AI Doesn’t Just Answer—It Writes Your Business Plan

TL;DR

• Oracle launched ‘agentic applications’ — a new category combining AI agents, automation, and business workflows into systems that generate strategies based on goals you set, not just features you click.
• The company evolved from embedding AI in apps to building over 1,000 agents, now delivering fully agentic apps across its enterprise suite including finance, HR, and supply chain.
• This positions Oracle against Salesforce Agentforce and other cloud providers racing to ship AI agents — but Oracle’s betting on outcome-driven systems, not chatbots.
• Steve Miranda, Oracle exec, frames it as a shift ‘from feature-based software to outcome-driven systems’ — you tell the system what you want, and AI drafts the plan.

Oracle Bets on Agentic Applications, Not Just AI Agents

Oracle just announced a new category it’s calling ‘agentic applications’ — software that blends AI agents, automation, and business workflows into systems designed around outcomes, not features. You don’t ask it to run a report. You tell it to optimize your supply chain, and it generates a strategy for you to approve or reject.

The company detailed the launch through Steve Miranda, an Oracle executive, who described the shift bluntly. ‘A major shift is the move from feature-based software to outcome-driven systems,’ he said.

Oracle didn’t just bolt a chatbot onto its ERP. It built over 1,000 agents across its suite — finance, HR, supply chain, customer experience — and now it’s wrapping them into applications that act more like strategic assistants than software tools. The idea: you set a business goal, the system drafts a plan, and you decide whether to execute.

Why Oracle’s Agentic Apps Gamble Matters for Enterprise Software

This isn’t a feature announcement. It’s a bet on how enterprise software gets used in three years.

Most AI integrations today are additive — a copilot here, a summarization tool there. Oracle’s pitch is subtractive. Instead of learning 50 features across five modules, you tell the system what you want and it figures out the features. That’s the theory, anyway.

And it’s a direct shot at the way companies like Salesforce are positioning Agentforce — as autonomous agents that handle tasks. Oracle’s framing is different. It’s not about agents handling tasks. It’s about applications that generate strategies. The distinction matters because it shifts the value proposition from ‘automate repetition’ to ‘draft decisions.’

Think of it like this — most AI tools today are sous chefs. They chop the vegetables and prep the mise en place. Oracle’s agentic apps want to be the chef de cuisine. They don’t just execute your recipe; they propose the menu based on what’s in the pantry and what your guests ordered last time.

I’ve watched enterprise software vendors chase AI integrations for two years, and most of it has been feature spam. Buttons that summarize emails. Dashboards with natural language queries. Useful, sure — but not transformative. If Oracle actually ships software where a procurement manager says ‘reduce supplier risk in APAC by 15%’ and the system drafts a three-option plan with trade-offs, that’s a different game.

But — and this is the big but — it only works if the AI doesn’t hallucinate supplier contracts or invent cost savings that don’t exist. Oracle’s building on top of its own structured data, which helps. But the gap between ‘generates a strategy’ and ‘generates a strategy you can trust’ is where this either transforms enterprise software or becomes expensive vaporware.

Oracle Built 1,000+ Agents Before Launching Agentic Apps

Oracle didn’t start here. The company spent years embedding AI into individual applications, then began building discrete agents — 50, then 100, now over 1,000 across its cloud suite. Those agents handle specific tasks: matching invoices, routing support tickets, flagging compliance risks.

The agentic applications layer sits on top of that foundation. Instead of interacting with individual agents, users interact with an application that orchestrates multiple agents toward a business outcome. It’s the difference between managing a team of specialists and hiring a general contractor who manages the specialists for you.

This evolution mirrors what’s happening across the enterprise software market. Salesforce is bundling agents into Agentforce. Microsoft is threading Copilot through every product. Google’s pushing Duet AI across Workspace and Cloud. Everyone’s racing to ship agents, but Oracle’s framing them as building blocks for something bigger — applications that think in outcomes, not tasks.

The competitive stakes are straightforward. If agentic applications work, Oracle has a narrative advantage: ‘We didn’t just add AI to software, we rebuilt software around AI.’ If they don’t, Oracle looks like it overpromised on a buzzword while Salesforce and Microsoft ship incremental improvements customers actually use.

Miranda acknowledged the confusion in the market. Customers are trying to figure out what AI agents even are, let alone how to deploy them at scale. Oracle’s answer is to stop selling agents and start selling applications that happen to use agents under the hood. That might cut through the noise — or it might just add a new layer of abstraction customers have to decode.

What This Signals About the Next Wave of Enterprise AI

Oracle’s launch is a signal that the first wave of enterprise AI — chatbots, summarization, search — is already considered table stakes. The next wave is about systems that generate options, not just answers. That’s a harder problem, and it’s where the real money lives.

If agentic applications catch on, enterprise software stops being a tool you learn and starts being a system you negotiate with. You state a goal, it drafts a plan, you approve or reject. That’s a fundamentally different interaction model, and it has implications for how companies train employees, how software gets priced, and how vendors compete.

It also raises questions about accountability. If an AI-generated supply chain strategy tanks your margins, who’s responsible? The user who approved it? The vendor who sold the system? The model that drafted the plan? Oracle’s betting that the value of speed and scale outweighs the risk, but enterprises are notoriously risk-averse when it comes to strategic decisions.

The broader trend is clear: AI is moving from augmentation to delegation. Not full autonomy — Oracle’s systems still put a human in the loop — but delegation nonetheless. You’re not asking the software to help you make a decision. You’re asking it to draft the decision and explain why.

That’s a big leap, and it’s happening faster than most enterprises are ready for. Oracle’s not the only company pushing this direction, but it’s one of the first to brand it as a distinct category and ship it across an entire product suite. Whether ‘agentic applications’ becomes a category or a footnote depends on execution — and on whether enterprises trust the plans these systems generate.

Three Things to Watch as Oracle Rolls Out Agentic Apps

First, watch how Oracle prices this. If agentic applications are just a rebrand of existing AI features, customers will see through it. If Oracle charges a premium for outcome-driven workflows, it needs to prove the ROI is real — not theoretical.

Second, watch the accuracy and trust metrics. Oracle’s building on structured enterprise data, which should reduce hallucination risk compared to general-purpose LLMs. But if a finance team gets burned by an AI-generated budget plan that missed a key constraint, trust evaporates fast. Oracle needs to ship transparency tools — audit trails, confidence scores, explainability layers — that let users understand why the system recommended what it did.

Third, watch the competitive response from Salesforce, Microsoft, and Google. If Oracle’s ‘agentic applications’ framing resonates with customers, expect the others to adopt similar language and ship their own outcome-driven systems within six months. If it doesn’t, Oracle risks looking like it’s over-engineering a problem customers don’t have yet. The market will decide whether this is the future of enterprise software or just another layer of AI hype.