Capacity development

Oracle launches embedded AI Agent Marketplace in Fusion Applications

Oracle has announced substantial enhancements to its AI Agent Studio for Fusion Applications, introducing a native AI Agent Marketplace, broader LLM support, and advanced agent tooling and governance features.

The AI Agent Marketplace is embedded within Fusion Applications, allowing customers to browse, test and deploy partner-built, Oracle-validated agents directly within their enterprise workflows. These agents can supplement or replace built-in agents to address industry-specific tasks.

Oracle is also expanding support for external large language models: customers and partners can now select from providers including OpenAI, Anthropic, Cohere, Google, Meta and xAI. This gives flexibility in choosing which LLM best fits a given use case.

New capabilities in Agent Studio include MCP support to integrate agents with third-party data systems, agent cards for cross-agent communication and collaboration, credential store for secure access to external APIs, monitoring dashboard, and agent tracing and performance metrics for observability.

It will also have prompt libraries and version control for managing agent prompts across lifecycles, workflow chaining and deterministic execution to organise multi-step agent tasks, and human-in-the-loop support to combine automation with oversight.

Oracle also highlights its network of 32,000 certified experts trained in building AI agents via Agent Studio. These experts can help customers optimise use, extend the marketplace, and ensure agent quality and safety.

Overall, Oracle’s release positions its Fusion ecosystem as a more open, flexible, and enterprise-ready platform for AI agent deployment, balancing embedded automation with extensibility and governance.

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New method helps AI models locate personalised objects in scenes

MIT and the MIT-IBM Watson AI Lab have developed a training approach that enables generative vision-language models to localise personalised objects (for example, a specific cat) across new scenes, a task at which they previously performed poorly.

While vision-language models (VLMs) are good at recognising generic object categories (dogs, chairs, etc.), they struggle when asked to point out your specific dog or chair under different conditions.

To remedy this, the researchers framed a fine-tuning regime using video-tracking datasets, where the same object appears in multiple frames.

Crucially, they used pseudo-names (e.g. ‘Charlie’) instead of real object names to prevent the model from relying on memorised label associations. This encourages it to reason about context, scene layout, appearance cues, and relative position, rather than shortcut to category matches.

AI models trained with the method showed a 12% average improvement in personalised localization. In some settings, especially with pseudo-naming, gains reached 21%. Importantly, this enhanced ability did not degrade the model’s overall object recognition performance.

Potential applications include smart home cameras recognising your pet, assistive devices helping visually impaired users find items, robotics, surveillance, and ecological monitoring (e.g. tracking particular animals). The approach helps models better generalise from a few example images rather than needing full retraining for each new object.

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Adaptive optics meets AI for cellular-scale eye care

AI is moving from lab demos to frontline eye care, with clinicians using algorithms alongside routine fundus photos to spot disease before symptoms appear. The aim is simple: catch diabetic retinopathy early enough to prevent avoidable vision loss and speed referrals for treatment.

New imaging workflows pair adaptive optics with machine learning to shrink scan times from hours to minutes while preserving single-cell detail. At the US National Eye Institute, models recover retinal pigment epithelium features and clean noisy OCT data to make standard scans more informative.

Duke University’s open-source DCAOSLO goes further by combining multiplexed light signals with AI to capture cellular-scale images quickly. The approach eases patient strain and raises the odds of getting diagnostic-quality data in busy clinics.

Clinic-ready diagnostics are already changing triage. LumineticsCore, the first FDA-cleared AI to detect more-than-mild diabetic retinopathy from primary-care images, flags who needs urgent referral in seconds, enabling earlier laser or pharmacologic therapy.

Researchers also see the retina as a window on wider health, linking vascular and choroidal biomarkers to diabetes, hypertension and cardiovascular risk. Standardised AI tools promise more reproducible reads, support for trials and, ultimately, home-based monitoring that extends specialist insight beyond the clinic.

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Elumelu calls for investment to harness AI development in Africa

Chairman of UBA Group Tony Elumelu told global financial leaders on Wednesday that AI could transform Africa’s healthcare, education, and agriculture sectors if inclusive development, skills, and access to capital are prioritised, as the continent risks being left behind in global AI development.

Elumelu stressed that Africa’s digital growth must focus on people. He praised the continent’s youthful population and creativity as its greatest assets, recalling how the Mobile Money app has managed to reshape African finance despite the lack of infrastructure.

He warned, however, that limited capital and digital skills continues to constrain progress. He called for ‘smart public–private partnerships’ to fund digital infrastructure and capacity development programmes, solutions that avoid adding to public debt. He pointed Heirs Holdings’ investments in energy and entrepreneurship as examples of long-term local value creation.

Elumelu also urged African governments to ensure their participation in global AI and data governance frameworks, noting that ‘inclusion is not automatic; it must be intentional’. He said the goal should be to ‘democratise prosperity’ by building systems that empower young people through technology and sustainable investment.

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AI system links hidden signals in patient records to improve diagnosis

Researchers at Mount Sinai and UC Irvine have developed a novel AI system, InfEHR, which creates a dynamic network of an individual’s medical events and relationships over time. The system detects disease patterns that traditional approaches often miss.

InfEHR transforms time-ordered data, visits, labs, medications, and vital signs, into a graphical network for each patient. It then learns which combinations of clues across that network tend to correlate with hidden disease states.

In testing, with only a few physician-annotated examples, the AI system identified neonatal sepsis without positive blood cultures at rates 12–16× higher than current methods, and post-operative kidney injury with 4–7× more sensitivity than baseline clinical rules.

As a safety feature, InfEHR can also respond ‘not sure’ when the record lacks enough signal, reducing the risk of overconfident errors.

Because it adapts its reasoning per patient rather than applying the same rules to all, InfEHR shows promise for personalized diagnostics across hospitals and populations, even with relatively small annotated datasets.

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Oracle and Microsoft partner to bring real-time AI insights into supply chains

Oracle announced a collaboration with Microsoft aimed at improving supply chain responsiveness and efficiency. The project centres on a new integration blueprint that bridges Oracle Fusion Cloud SCM with Microsoft Azure IoT Operations and Microsoft Fabric.

Under this plan, sensor and equipment data from factory floors is captured in real time via Azure IoT and forwarded through Fabric. That data will then feed directly into Oracle SCM workflows.

The goal: more visibility, faster decisions and automated responses, such as triggering maintenance, quality checks or inventory adjustments.

Among the features highlighted are secure, real-time intelligence and data flows from shop floor equipment into enterprise systems, automated business events that respond to changes (e.g. imbalance, faults, demand shifts), standardised best practices with reference architectures and prescriptive guidance for integration and embedded AI assistant capabilities in SCM to augment decision making and resilience.

Oracle frames this as part of its Smart Operations vision, where systems are more connected and responsive by design. Microsoft emphasises that Azure’s edge processing and Fabric’s real-time analytics are critical to turning raw IoT signals into actionable business events.

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Microsoft to support UAE investment analytics with responsible AI tools

The UAE Ministry of Investment and Microsoft signed a Memorandum of Understanding at GITEX Global 2025 to apply AI to investment analytics, financial forecasting, and retail optimisation. The deal aims to strengthen data governance across the investment ecosystem.

Under the MoU, Microsoft will support upskilling through its AI National Skilling Initiative, targeting 100,000 government employees. Training will focus on practical adoption, responsible use, and measurable outcomes, in line with the UAE’s National AI Strategy 2031.

Both parties will promote best practices in data management using Azure services such as Data Catalog and Purview. Workshops and knowledge-sharing sessions with local experts will standardise governance. Strong controls are positioned as the foundation for trustworthy AI at scale.

The agreement was signed by His Excellency Mohammad Alhawi and Amr Kamel. Officials say the collaboration will embed AI agents into workflows while maintaining compliance. Investment teams are expected to gain real-time insights and automation that shorten the time to action.

The partnership supports the ambition to make the UAE a leader in AI-enabled investment. It also signals deeper public–private collaboration on sovereign capabilities. With skills, standards, and use cases in place, the ministry aims to attract capital and accelerate diversification.

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Agentic AI at scale with Salesforce and AWS

Salesforce and AWS outlined a tighter partnership on agentic AI, citing rapid growth in enterprise agents and usage. They set four pillars for the ‘Agentic Enterprise’: unified data, interoperable agents, modernised contact centres and streamlined procurement via AWS Marketplace.

Data 360 ‘Zero Copy’ accesses Amazon Redshift without duplication, while Data 360 Clean Rooms integrate with AWS Clean Rooms for privacy-preserving collaboration. 1-800Accountant reports agents resolving most routine inquiries so human experts focus on higher-value work.

Agentforce supports open standards such as Model Context Protocol and Agent2Agent to coordinate multi-vendor agents. Pilots link Bedrock-based agents and Slack integrations that surface Quick Suite tools, with Anthropic and Amazon Nova models available inside Salesforce’s trust boundary.

Contact centres extend agentic workflows through Salesforce Contact Center with Amazon Connect, adding voice self-service plus real-time transcription and sentiment. Complex issues hand off to representatives with full context, and Toyota Motor North America plans automation for service tasks.

Procurement scales via AWS Marketplace, where Salesforce surpassed $2bn in lifetime sales across 30 countries. AgentExchange listings provide prebuilt, customisable agents and workflows, helping enterprises adopt agentic AI faster with governance and security intact.

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New Cisco study shows most companies aren’t AI-ready

Most firms are still struggling to turn AI pilots into measurable value, Cisco’s 2025 AI Readiness Index finds. Only 13% are ‘AI-ready’, having scaled deployments with results. The rest face gaps in data, security and governance.

Southeast Asia outperforms the global average at 16% ready. Indonesia reaches 23% and Thailand 21%, ahead of Europe at 11% and the Americas at 14%. Cisco says lower tech debt helps some emerging markets leapfrog.

Infrastructure debt is mounting: limited GPU capacity, fragmented data and constrained networks slow progress. Just 34% say their tech stack can adapt and scale for evolving compute needs. Most remain stuck in pilots.

Adoption plans are ambitious: 83% intend to deploy AI agents, with almost 40% expecting them to support staff within a year. Yet only one in three have change-management programmes, risking stalled workplace integration.

The leaders pair strong digital foundations with clear governance and cybersecurity embedded by design. Cisco urges broader collaboration among industry, government and tech firms, arguing that trust, regulation and investment will determine who monetises AI first.

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Growth of AI increases water and energy demands

AI data centres in Scotland use enough tap water to fill over 27 million half-litre bottles annually, BBC News reports. The number of centres has quadrupled since 2021, with AI growth increasing energy and water use, though it remains a small fraction of the national supply.

Scottish Water urges developers to adopt closed-loop cooling or treated wastewater instead of relying only on mains water. Open-loop systems, still used in many centres, consume vast amounts of water, but closed-loop alternatives can reduce demand, though they may increase energy usage.

Experts warn that AI data centres have a significant carbon footprint as well. Analysis from the University of Glasgow estimates the energy use of Scottish centres could equate to each person in the country driving an extra 145 kilometres per year.

Academic voices have called for greater transparency from tech companies and suggested carbon targets and potential penalties to ensure sustainable growth.

The Scottish government and industry stakeholders are promoting ‘green’ AI development, citing Scotland’s cool climate, renewable energy resources, and local expertise. Developers are urged to balance AI expansion with Scotland’s net zero and resource sustainability goals.

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