According to DeepLearning.AI, Oracle will host a workshop at AI Dev x SF focused on agent memory and building agents that learn, adapt, and operate reliably in production. As reported by DeepLearning.AI on Twitter, the session addresses practical strategies such as long-term memory stores, retrieval augmented generation, and feedback loops for continuous adaptation in enterprise workflows. According to DeepLearning.AI, this creates business opportunities to deploy autonomous and semi-autonomous agents for customer support, IT operations, and data workflows with improved reliability and observability.

According to AndrewYNg on X, DeepLearning.AI launched a short course titled "Agent Memory: Building Memory-Aware Agents," developed with Oracle and taught by Richmond Alake and Nacho Martínez, focused on persistent agent memory across sessions. As reported by DeepLearning.AI, the curriculum covers designing a Memory Manager for episodic, semantic, and procedural memory, implementing semantic tool retrieval to load only relevant tools at inference time without bloating context, and building write-back pipelines so agents autonomously update knowledge over time. According to the course page, the skills target production use cases like research agents that work over multiple days, enabling scalable retrieval, lower context costs, and improved task continuity for enterprise agents.

According to DeepLearning.AI on X, a new short course titled Agent Memory: Building Memory-Aware Agents teaches how to design memory systems that let AI agents store, retrieve, and refine knowledge across sessions, taught by Richmond Alake and Nacho Martínez. As reported by DeepLearning.AI, the Oracle-collaborated curriculum focuses on practical architectures for long-term memory, retrieval augmented generation, vector databases, and session persistence to improve agent reliability and personalization. According to DeepLearning.AI, the business impact includes faster prototyping of production-grade assistants, better customer support bots through persistent user context, and reduced inference costs via efficient memory retrieval. As noted by DeepLearning.AI, enrollment details were announced alongside the course launch on March 18, 2026.

According to DeepLearning.AI on X, the organization launched a short course titled "Agent Memory: Building Memory-Aware Agents" in collaboration with Oracle, taught by Richmond Alake and Nacho Martínez, focusing on designing memory systems that let AI agents store, retrieve, and refine knowledge across sessions (source: DeepLearning.AI post on X, March 18, 2026). As reported by DeepLearning.AI, the curriculum emphasizes practical techniques such as vector database retrieval, embedding selection, memory indexing, and long-term context management for production agents, aiming to reduce hallucinations and improve task continuity in multi-session workflows (source: DeepLearning.AI post on X). According to the announcement, business teams can leverage these memory patterns to power customer support copilots, autonomous RAG pipelines, and CRM-integrated assistants where persistent memory drives higher retention and lower support costs (source: DeepLearning.AI post on X).

According to Sawyer Merritt on X, Oracle and OpenAI scrapped plans to expand a flagship AI data center in Texas after negotiations dragged over financing and OpenAI’s changing needs; as reported by Sawyer Merritt, the stalled talks indicate shifting compute roadmaps and capital allocation priorities that could redirect OpenAI workloads to alternative hyperscale regions or multi-cloud partners, impacting GPU supply planning, colocation demand, and enterprise AI hosting strategies. According to Sawyer Merritt, the cancellation highlights near-term risks for large-scale AI infrastructure projects tied to fast-moving model requirements and cost structures, creating opportunities for other cloud providers and data center operators to capture OpenAI-adjacent demand with flexible financing, modular buildouts, and expedited power procurement.

According to Greg Brockman (@gdb) on Twitter, OpenAI is partnering with Oracle and SoftBank to build five new Stargate sites, further expanding AI supercomputing infrastructure. This collaboration aims to boost the scalability and efficiency of large language models and next-generation AI applications, with Oracle providing cloud infrastructure and SoftBank contributing to global deployment. The new sites are expected to accelerate enterprise AI adoption, improve model training speeds, and unlock new business opportunities for AI-driven services (source: @gdb, x.com/OpenAI/status/1970601342680084483).

According to OpenAI, the company has announced five new Stargate AI compute sites in partnership with Oracle and SoftBank, putting them ahead of schedule on their previously stated 10-gigawatt commitment for AI infrastructure expansion (source: OpenAI Twitter, openai.com). This move highlights the growing need for massive computing power to support advanced AI models and signals significant business opportunities for cloud infrastructure providers. The partnership is expected to accelerate AI deployment, enabling enterprises to leverage next-generation AI applications and large language models at scale.

According to DeepLearning.AI, OpenAI is partnering with Oracle to build a massive new data center infrastructure, adding 4.5 gigawatts of capacity as part of their Stargate program. The Wall Street Journal reports that OpenAI will pay Oracle $30 billion annually for this collaboration. This move follows the recent launch of a 1.2-gigawatt data center in Abilene, Texas. The expanded capacity aims to meet the soaring demand for advanced AI model training and deployment, unlocking new business opportunities for enterprise AI solutions and cloud infrastructure providers. The scale of this investment signals rapid growth in the AI data center market and positions both OpenAI and Oracle as leaders in delivering next-generation AI services. (Source: DeepLearning.AI on Twitter, The Wall Street Journal)