
Gemini Enterprise Agent Platform is Google Cloud’s next-generation system for designing and managing advanced AI agents across the enterprise. Built as the successor to Vertex AI, it unifies model selection, development, and deployment into a single scalable environment. The platform supports a vast ecosystem of over 200 AI models, including Google’s latest Gemini innovations and popular third-party models. It offers flexible development tools like Agent Studio for visual workflows and the Agent Development Kit for deeper customization. Businesses can deploy agents that operate continuously, maintain long-term memory, and handle multi-step processes with high efficiency. Security and governance are central, with features such as agent identity verification, centralized registries, and controlled access through gateways. The platform also enables seamless integration with enterprise systems, allowing agents to interact with data, applications, and workflows securely. Advanced monitoring tools provide real-time insights into agent behavior and performance. Optimization features help refine agent logic and improve accuracy over time. By combining automation, intelligence, and governance, the platform helps organizations transition to autonomous, AI-driven operations. It ultimately supports faster innovation while maintaining enterprise-grade reliability and control.
Learn more
NeuBird is the Agentic Operations Center: one secure link to your telemetry and LLMs that resolves incidents, remembers every investigation, and shares one governed truth across your teams and agents.
Learn more
CMEM Cloud
CMEM Cloud serves as the synchronization layer for claude-mem, designed to connect AI agent memory universally via a single private MCP link. The open-source engine, claude-mem, records notes while an agent performs tasks, while CMEM Cloud replicates that local memory, enabling agents to access it seamlessly across different sessions, devices, editors, and any MCP-compatible client. This innovative system eliminates the need for users to repetitively clarify context, copy previous notes, or start from scratch by automatically logging decisions, bug fixes, dead ends, environmental observations, architectural decisions, and other structured insights as the agent operates. These valuable insights are preserved in a temporal database, allowing for meaning-based searches through vector recall, and are accessible via a private MCP endpoint that any compatible agent can utilize for reading and writing. The process initiates with the installation of the local engine, followed by allowing a secondary model to generate structured notes independently, syncing the local database with CMEM Cloud, and finally enabling memory recall from any location. This approach not only enhances efficiency but also fosters a more collaborative environment among agents by sharing insights effortlessly.
Learn more
Memmy
Memmy serves as a local-first AI memory framework that ensures all AI tools maintain a unified representation of the user. Designed for individuals who frequently collaborate with multiple assistants, it seamlessly interprets authorized collaboration histories from platforms like Cursor, Claude, and Codex, transforming disjointed dialogues, user preferences, project details, technical choices, achievements, and common challenges into an organized memory system. The workflow consists of three distinct phases: Scan reviews chosen histories stored on the user's device; Organize processes and refines the information by deduplication, categorization, and indexing; and Inject provides the active AI with only the most pertinent memories through targeted, real-time matching rather than overwhelming it with excessive data. This intelligent structure enables users to shift between tools effortlessly while retaining context, consolidate discussions held with various agents, document recent choices made, maintain writing styles, and proceed with tasks that are yet to be completed, all of which enhances productivity. As a result, users can navigate their work with greater efficiency and less disruption.
Learn more