Your Google AI stack can already do real work.
Gemini can reason over information and use tools to act. Google also supplies workplace AI, enterprise connectors, and infrastructure for building and running agents. For a company already using Google Cloud, those are substantial reasons to start there.
The useful comparison with Monarch is how each approach gives an agent dependable access to your particular applications. A Google implementation can assemble that access from existing connectors and custom tools. Monarch discovers application behavior and turns verified paths into reusable actions through its Product Graph.
If your Google tools already cover the workflow, you may have everything you need. Monarch becomes more relevant when the work reaches custom fields, internal applications, or operations that your existing tools do not expose.
There are several different Google products in this decision
“We have Gemini” can describe very different starting points. An employee using AI in Gmail has a different implementation from an engineering team deploying a custom agent on Google Cloud.
| Google layer | What it supplies | What to establish for your workflow |
|---|---|---|
| Gemini models and Gemini API | Model intelligence and function calling for external tools | Which tools execute requests and which application operations they expose |
| Google Workspace with Gemini | AI assistance within workplace applications such as Gmail, Docs, and Sheets | Whether the task fits the available features and your Workspace configuration |
| Gemini Enterprise app | An employee interface for enterprise AI, connected information, and agents | Which connectors, actions, and custom integrations your edition and administrators enable |
| Gemini Enterprise Agent Platform | Model and agent development, deployment, evaluation, and governance infrastructure | How your team connects the business applications and owns the resulting workflow |
Google documents function calling as a way to connect Gemini to external APIs and execute actions. Its Workspace AI offering brings assistance into employees' everyday applications.
The Gemini Enterprise connector catalog includes third-party systems, supported write operations, and custom MCP connections. Coverage varies by connector and edition, so a product logo alone does not establish that a particular operation is available.
Teams familiar with Vertex AI should also account for Google's naming changes. Google now calls the platform Gemini Enterprise Agent Platform. Its name-change reference maps the previous Vertex AI terminology to the current names.
The Agent Platform overview describes Agent Development Kit, Agent Runtime, evaluation, and governance capabilities. Google gives engineering teams considerable room to build. That makes a serious Google-based implementation a credible alternative.
Where Monarch changes the implementation
A function that updates an order needs a working implementation behind it. Somebody has to establish which record represents the order, which fields can change, and what the application requires before accepting the update.
Sometimes a supported connector already handles all of that. Sometimes your engineers can expose an existing internal service with little additional work. Both are good outcomes.
The difficult cases appear deeper inside the application. A release operation may depend on a custom approval record. A familiar screen may call an undocumented endpoint. The relevant behavior can exist in the software while remaining absent from the tools your agent currently receives.
Monarch's Product Graph maps application structure and behavior through discovery, including APIs, UI paths, schemas, and permissions. It turns discovered and verified operations into actions that workflows can reuse. Coverage is limited to what the connected account can access and what discovery captures and verifies.
That is the incremental value to test alongside the intelligence you already use: how much application access becomes available, how well it matches the actual system, and how much tool work your team still owns.
| Evaluation point | Google stack with enabled tools | Monarch approach |
|---|---|---|
| Existing supported operation | Use the available connector or tool after configuration and validation | Establish discovered and verified coverage for the required action |
| Custom operation | Extend a tool, expose an internal service, or implement another access path | Discover the operation where accessible and represent its verified path in the Product Graph |
| Reuse | Share connector configurations and tool implementations across agents | Reuse mapped application actions across workflows |
| Application changes | Provider or internal tool owner updates and validates the affected implementation | Maintain the application map and revalidate affected paths; test the changed operation before relying on it |
| Access boundary | Application credentials, tool authorization, and configured platform controls | Connected-account permissions and verified discovery coverage, with workflow controls |
| Best starting condition | Existing Google tools cover the process, with an owner for extensions | Application depth and repeated integration work are blocking useful workflows |
Engineers can build sophisticated application access on Google's platform. The comparison concerns the work required to establish and maintain that access. Our guide to connecting agents to internal applications explains the implementation choices in more detail.
Use Google's intelligence across more of the business
Your Google estate may already connect mail, documents, and selected enterprise data. The opportunity expands when agents can also operate the internal systems where the work finishes. Monarch discovers those application operations and brings them into cross-system agentic workflows, including the dependencies and checks between steps.
Google can remain the model and agent environment. Monarch adds discovery, maintained operational access, and workflow orchestration around the connected systems. The implementation needs agreed tool interfaces and identities at that boundary.
Mapping a new product with Monarch typically takes hours. Once the relevant operations are mapped, inference can assemble new workflows in minutes. That can make the second purchasing workflow much easier to build than starting another integration from scratch. Access, complexity, approvals, and testing still affect deployment time.
Established workflow patterns provide a starting point for the business process. For example, match the order, establish the exception, obtain approval, and verify the write. Adapt the pattern to your purchasing policy and actual application behaviour before expanding its use.
Test a supplier delivery exception across systems
Consider an illustrative purchasing workflow. A supplier emails a revised delivery date. The purchasing team needs to check the purchase order in its ERP, compare the change with a warehouse receiving appointment, and open an exception ticket if the revised date misses the agreed window.
The final step records the approved decision on the purchase order. In this example, that means updating a custom delivery-exception object linked to the order.
With Google's stack, start with the supported mail and ticketing capabilities. Give the agent approved tools for reading the ERP order, checking the warehouse appointment, and writing the decision. If those tools already cover the custom object and its validation rules, the implementation may be straightforward.
If the ERP connector exposes standard orders but omits the exception object, the engineering task becomes specific: create and maintain access to that operation. Gemini can help reason through the case and call a supplied tool. The tool still needs a valid way to perform the update.
A Monarch evaluation would establish whether discovery can reach and verify the exception object and update path under the supplied account. If it can, that mapped operation becomes reusable access for this workflow and others that need the same object.
Run both implementations against an ordinary case, a missing appointment, an ambiguous order match, and a denied update. Require a reviewer before saving the decision. Read the order back afterward to confirm the saved state.
These checks reveal whether the workflow completes correctly and where a person still intervenes. They also expose permission boundaries that a successful summary can hide.
Choose based on the work left to own
Start with Google when your employees work primarily in Workspace, your required enterprise actions are covered, or you have a platform team equipped to maintain custom tools.
Evaluate Monarch when each new workflow repeats the same application investigation: finding fields, understanding dependencies, establishing write paths, and fixing access when the product changes. The case becomes stronger as those discovered operations serve additional workflows.
Keep model capability comparable during the evaluation. Measure correct completed work, manual interventions, and the effort to add a second workflow. Include ownership after the pilot in the decision, especially for custom tools.
Talk to Monarch about one workflow your current Google tools cannot complete, and use its exact operations as the scope of a practical evaluation.
Frequently asked questions
Does having Gemini remove the need for Monarch?
It can remove the need for additional infrastructure when your configured tools already cover the task. Monarch is worth evaluating when application access and its upkeep remain the obstacle.
Can Google's agents update third-party systems?
Yes. Gemini can use external functions, and the Gemini Enterprise app supports actions through applicable connectors. The exact operation depends on the connector or implementation, configuration, and authorization.
Is Monarch a replacement for Vertex AI?
Monarch discovers and maintains application operations and orchestrates workflows across them. Google's platform, now named Gemini Enterprise Agent Platform, supplies model and agent infrastructure. They can serve complementary roles; define where the workflow runs and how each application action is supplied.
Can Monarch work alongside a Google-based agent?
That is a reasonable architecture to evaluate: retain the agent's intelligence and add reusable application actions. Confirm the integration interface and supported configuration for your deployment before assuming compatibility.
Does discovery grant access to restricted records?
No. Monarch's coverage depends on the connected account's permissions and the paths discovery captures and verifies. An inaccessible operation requires an authorized access change or a different workflow design.