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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StackAI is an enterprise AI automation platform to build end-to-end internal tools and processes with AI agents in a fully compliant and secure way. Designed for large, regulated organizations, it enables teams to automate complex workflows across operations, compliance, finance, IT, and support without heavy engineering.
With StackAI you can:
• Connect knowledge bases (SharePoint, Confluence, Notion, Google Drive, databases) with versioning, citations, and access controls
• Publish AI agents as chat assistants, advanced forms, or APIs integrated into Slack, Teams, Salesforce, HubSpot, or ServiceNow
• Govern usage with enterprise security: SSO (Okta, Azure AD, Google), RBAC, audit logs, PII masking, data residency, and cost controls
• Route across OpenAI, Anthropic, Google, or local LLMs with guardrails, evaluations, and testing
• Deploy in multi-tenant cloud, dedicated cloud, private cloud, or on-premise
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Claude Managed Agents
Claude Managed Agents is a pre-built, configurable agent system from Anthropic designed to run long-running, asynchronous tasks on managed infrastructure without requiring developers to build their own agent loops. It acts as a complete “agent harness,” allowing developers to define goals while the system handles execution, orchestration, and state management behind the scenes. Unlike direct model prompting, which requires step-by-step interaction, Managed Agents are designed for tasks that unfold over time, such as research, automation, or multi-step workflows, where the agent can continue working independently after being started. It supports advanced capabilities such as multi-agent orchestration, where a primary agent can coordinate specialized sub-agents that operate in parallel with isolated contexts, improving both speed and output quality.
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