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Message: [QUOTE="Bombastus, post: 91875, member: 2178"] Google launched Gemini Enterprise Agent Platform in April 2026 as the evolution of Vertex AI, adding dedicated agent orchestration and governance capabilities. The naming can make the stack look flatter than it really is. Gemini Enterprise is now an umbrella for an employee-facing app and a deeper developer platform, with each layer solving a different part of the same deployment problem. The Gemini Enterprise app is the front door for people using agents at work. Employees can discover approved agents, create simpler ones without code, share them, and manage longer-running work from one governed environment. Technical teams drop lower into Agent Platform when they need custom models, code, orchestration, runtime controls, evaluation, or infrastructure decisions. For companies following [B][URL='https://goldmidi.com/community/threads/tech-mahindra-teams-with-google-to-unleash-agentic-ai.63808/']Tech Mahindra’s Google Cloud AI deployment[/URL][/B], this distinction matters because buying access to Gemini Enterprise does not collapse every AI task into one interface. A finance employee asking an agent to investigate a report has a very different job from the engineer building, testing, securing, and operating the agent that performs the investigation. [HEADING=2]The app is where employees meet the agents[/HEADING] Gemini Enterprise app is designed around consumption and lightweight creation. Google positions it as a single place where knowledge workers can find first-party, partner-built, and company-created agents, while administrators control which ones employees are allowed to use. Agent Designer gives nondevelopers a no-code route for creating narrower helpers without opening a development environment. The app can also expose agents built elsewhere. A team can create a sophisticated agent with code in Agent Platform, register it, govern it, then make it available inside Gemini Enterprise for ordinary employees. This separation keeps the employee experience relatively simple without forcing developers to build within the limits of a workplace chat interface. Long-running agents make the boundary clearer. Gemini Enterprise includes an Inbox where users can monitor work that continues beyond a single conversation, while the platform beneath it handles the execution and governance machinery. The employee sees the assignment and result. Engineering teams deal with runtime behavior, permissions, integrations, failures, and observability. [HEADING=2]Agent Platform is where the engineering happens[/HEADING] Agent Platform inherited the model selection, model building, tuning, and development capabilities associated with Vertex AI, then added a much heavier agent layer. Developers can work in Agent Studio for lower-code development or move into Agent Development Kit when they need explicit tools, state, memory, sub-agents, and programmatic control. Google even allows Agent Studio logic to be exported into ADK when a prototype outgrows the visual environment. Deployment no longer stops at hosting a model endpoint. Agent Runtime runs stateful agents, while Agent Identity and Agent Gateway provide controls around what those agents can access and how they communicate. Evaluation, observability, registries, sandboxes, orchestration, and governance now sit in the same broader platform rather than requiring every team to assemble them separately. Interoperability is another reason the developer layer exists. Gemini Enterprise supports external agents and open protocols rather than requiring every participant in a workflow to use one model or framework. A [B][URL='https://arxiv.org/abs/2607.23884']recent empirical comparison of MCP and A2A[/URL][/B] found meaningful engineering trade-offs between lightweight tool-oriented coordination and richer stateful agent-to-agent communication, which is exactly the kind of choice platform teams face once workflows cross product boundaries. [HEADING=2]BigQuery supplies context rather than replacing the platform[/HEADING] BigQuery occupies another layer that gets blurred in broad descriptions of Google's AI stack. It can ground agents in governed enterprise data, expose data through managed MCP services, and support specialized data agents, but it is not the employee agent directory or the general-purpose agent development environment. Its job is closer to making structured business information usable by agents without stripping away data controls. Google now lets conversational analytics and research experiences surface BigQuery-backed intelligence through Gemini Enterprise. The employee can ask a business question in the front-end experience while the underlying agent reaches governed data services behind the scenes. Keeping those responsibilities separate means analysts can maintain semantic definitions and access policies without asking every employee to work directly inside a data warehouse console. The same pattern applies to models. Agent Platform provides access to Gemini models and a wider Model Garden, but the model is only one component inside a production agent. Tools, enterprise data, permissions, memory, runtime infrastructure, evaluation, and interaction surfaces determine what the finished system can actually do. Vertex AI therefore did not simply disappear and leave customers choosing between two competing Google AI products. Its core development role was absorbed and expanded inside Gemini Enterprise Agent Platform, while the Gemini Enterprise app became the governed workplace surface above it. Choosing the right layer starts with who is doing the work, because an employee running an approved agent and an engineer building one should not need the same controls, interfaces, or level of access. [/QUOTE]

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