Oracle AI: Agentes para Saúde

OCI delivers a highly secure, low-latency alternative to legacy clouds by pairing bare-metal compute with a flat, non-blocking network architecture. It stands out by running Oracle's flagship Autonomous Database natively, while offering massive AI training clusters powered by tens of thousands of NVIDIA GPUs. With uniform pricing across all global regions and flexible deployment options (including dedicated on-premises regions starting at three racks), OCI gives enterprises a predictable, highly performant home for their most demanding workloads.

Oracle Autonomous Database eliminates manual database administration by automating provisioning, tuning, patching, and scaling. Running on Oracle's Exadata infrastructure, it leverages machine learning to deliver 99.995% availability (less than 30 minutes of planned and unplanned downtime per year) while automatically blocking security threats. Teams can deploy specialized workloads (including transaction processing, data warehousing, and JSON-centric applications) and scale compute and storage resources up or down instantly with zero downtime.

Oracle AI Agent Memory transforms stateless AI agents into stateful enterprise systems by embedding a persistent memory layer directly into the Oracle AI Database. Using a unified Python SDK (oracleagentmemory), developers can store and retrieve critical context across sessions through two primary pillars: short-term memory for active threads and long-term memory for user preferences, learned rules, and historical facts. By combining vector similarity search, relational tables, and JSON metadata within Oracle's converged database infrastructure, the technology eliminates the need for fragmented memory stacks while maintaining enterprise-grade security and data governance.

The platform streamlines enterprise automation by letting developers provision AI agents using declarative configurations. These agents connect directly to enterprise data sources through built-in Retrieval-Augmented Generation (RAG) and natural language-to-SQL (NL2SQL) engines. By utilizing the Model Context Protocol (MCP) and integrating with Oracle Integration Cloud (OIC), the platform allows agents to orchestrate complex workflows, invoke custom APIs, and execute real-time transactions across systems like ERP and HCM while maintaining strict data privacy boundaries.

RAG is a critical GenAI architecture: it solves the LLM 'hallucination' problem by inserting a retrieval step before generation. A user query is vectorized, then used to query an external knowledge base (e.g., a Pinecone vector database) for relevant document chunks (typically 512-token segments). These retrieved facts augment the original prompt, providing the LLM (e.g., Gemini or Llama 3) the specific, current, or proprietary context required. This process ensures the final response is accurate and grounded in domain-specific data, avoiding the high cost and latency of full model retraining.