Gemini Enterprise Agent Platform is a comprehensive solution from Google Cloud designed to help organizations build, scale, govern, and optimize AI agents. It represents the evolution of Vertex AI, combining advanced model development with new capabilities for agent orchestration and integration. The platform provides access to over 200 leading AI models, including Google’s Gemini series and third-party options like Anthropic’s Claude. It enables teams to create intelligent agents using both low-code and code-first development environments. With features like Agent Runtime and Memory Bank, businesses can deploy long-running agents that retain context and perform complex workflows. The platform emphasizes security and governance through tools like Agent Identity, Agent Registry, and Agent Gateway. It also includes optimization tools such as simulation, evaluation, and observability to ensure consistent agent performance.
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Teradata VantageCloud: The complete cloud analytics and data platform for AI.
Teradata VantageCloud is an enterprise-grade, cloud-native data and analytics platform that unifies data management, advanced analytics, and AI/ML capabilities in a single environment. Designed for scalability and flexibility, VantageCloud supports multi-cloud and hybrid deployments, enabling organizations to manage structured and semi-structured data across AWS, Azure, Google Cloud, and on-premises systems. It offers full ANSI SQL support, integrates with open-source tools like Python and R, and provides built-in governance for secure, trusted AI. VantageCloud empowers users to run complex queries, build data pipelines, and operationalize machine learning models—all while maintaining interoperability with modern data ecosystems.
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ReinforceNow
ReinforceNow is an end-to-end platform for continual learning with AI agents, built to help teams deploy, train, and repeat. It lets developers build AI agents and continuously train them on production traffic, or let Claude Code help set it up automatically. It handles reinforcement learning infrastructure, experiment orchestration, agent versioning, GPU training logic, and telemetry, so teams can focus on agent logic, data collection, and rewards. ReinforceNow supports fast LLM fine-tuning with LoRA, high-throughput training, and wide model support for open source models like Qwen, DeepSeek, and GPT-OSS. It provides advanced telemetry to evaluate, monitor, and iterate on AI agent LLM applications, with traces, rewards, experiment metrics, and training observability. Teams can train on long-horizon tasks with 32k to 1 million context size, build vertical agents for multi-turn and long-running tasks, and use rich tooling for reinforcement learning workflows.
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