Oracle lets AI agents do the work, provided you stay in Big Red's world

Oracle's new Fusion Claw runtime could give customers more control over AI agents automating business processes, analysts say.

But they warn that adoption remains at an early stage and customers risk becoming more tightly tied to Oracle's applications and cloud.

Having promised "applications that can reason, decide, and act in pursuit of defined business objectives" back in March, Big Red has now introduced a runtime to govern their actions, running on its cloud and integrated with its Fusion applications.

Oracle is targeting ERP, finance, and supply chain management with Fusion Claw for cloud-based Fusion applications running on Oracle Cloud Infrastructure (OCI).

It will be powered by frontier models including those from Google and OpenAI, with support for others planned over time.

Oracle CEO Mike Sicilia said the company was trying to address the problem of getting "measurable value for our business" from AI investment.

"Fusion Claw helps answer that question by moving from AI assistance to execution, enabling customers to automate increasingly complex work across their most important business processes, with enterprise-grade governance," he said.

Analysts welcomed the move as a path that offers customers control and assurance in their AI agent implementations on Oracle Fusion Cloud Applications.

A recent Gartner "first take" on Fusion Claw said the technology was a way to provision "isolated containers to execute heavy supply chain optimizations and subledger reconciliations within clear policy limits" rather than running local desktop scripts or chatbot prompts.

"Instead of bolting AI onto legacy transactional back ends as a sidecar copilot or UI wrapper, Oracle is rebuilding its application foundation around native agentic workflows," Gartner said.

Other vendors should take heed of the move, the analyst said, because customers need to get away from prompt-based UIs and sidecar chatbots that only address routine data entry and basic task automation and offer "diminishing returns" that fail to justify enterprise AI spending.

"AI horsepower should be pointed at the most complex operational challenges that were previously unsolvable inside core ERP systems, such as multi-echelon supply chain visibility, network design, sales and operations planning (S&OP), logistics routing, and load optimization," Gartner said.

However, users trying to get these kinds of enterprise-scale applications hit a cost barrier because using an LLM to address thousands or millions of "transactional records, line items, or constraint variables… quickly becomes financially unviable at enterprise scale," it said.

Oracle gets around the problem by separating reasoning from compute execution.