You can build enterprise agents directly with Claude or OpenAI. The buying decision is how much application integration your team wants to own.
Both providers let agents call tools that read and change business records. Your engineers can supply those tools through existing APIs, MCP servers, or custom code. Computer use adds another route through an application's interface. A serious comparison starts with that complete setup.
Monarch adds an AI agent integration platform around the application work. Its Product Graph discovers how connected applications operate and makes verified paths available for agents to reuse. Evaluate it when reaching the necessary actions, coordinating work across products, and maintaining access consume a substantial part of the project.
Keep the model choice separate from that decision. An organisation can use frontier models and buy integration infrastructure for the work those models need to perform.
What building directly already gives you
OpenAI supports custom function calls, connectors, and remote MCP tools. An agent can request actions, pass arguments, and use the returned results to continue its work. Claude supports client tools executed by your application and server tools with their own execution arrangements. OpenAI tools and MCP, Claude tool use
A tool can contain substantial tested business logic. Your developers can put an entire sequence behind one operation, reuse it across agents, and maintain it in source control. Building directly does not require the model to invent every click or calculation on every run.
This can be a sensible choice when the required operations are already exposed, the workflow is narrow, and a team owns the integration. Adding another platform needs to justify its cost and operating responsibility.
The more revealing test is an operation your current tools cannot perform. For example, changing an order's delivery address may require a custom fulfilment screen that the public API does not expose. Somebody has to establish a permitted path to that action and prove what it changes.
Compare what your team will build and maintain
| Part of the job | Building directly with model tools | Adding Monarch |
|---|---|---|
| Interpret the request | Configure the model and evaluate its decisions | Retain the chosen agent intelligence and evaluate it against the same job |
| Reach application actions | Use existing tools or implement the missing operations | Discover and verify application operations through Product Graph, within permitted coverage |
| Preserve application knowledge | Maintain tool code, prerequisites, and checks in your implementation | Reuse discovered application knowledge and verified paths across the work |
| Handle application changes | Investigate failed operations, update implementations, and rerun checks | Evaluate Monarch's maintenance and self-healing on the actual paths used |
| Govern the business process | Define identity, approval, and exception rules, then enforce them in execution | Define the same rules and verify how the configured workflow enforces them |
| Prove the outcome | Check the resulting records and reconcile partial completion | Apply the same completion standard across the connected systems |
This comparison concerns where the application work lives and who maintains it. Every approach still needs a business owner who can decide what the agent is allowed to do and what counts as success.
Follow one address change across the business
Consider an illustrative customer request to change a default delivery address and redirect one pending order. The customer profile, fulfilment application, and service desk hold different parts of the job.
The agent needs to identify the right account, check whether the order can still change, obtain any required approval, update the relevant records, and confirm the result. Changing the profile alone can leave the parcel going to the old address.
The service case should record both updates so the next person can see what changed.
With a direct build, inventory the available operations first. A supported profile API may cover the first write. An existing fulfilment connector may cover the second. If both work, your team can concentrate on the business sequence and its exceptions.
If the fulfilment action is missing, the integration work expands. Your engineers must investigate the application, establish authorised access, and implement the operation. That is the point to compare another custom integration with application discovery through Monarch.
Product Graph captures application operations and their requirements, including public APIs, private web operations, and UI paths where available. Its reach is bounded by the connected account and what discovery captures and verifies. For older environments, including AS/400 terminal workflows, evaluate the actual application and connection setup as part of the scope.
The useful result is a callable, verifiable delivery-change operation that the agent can use again. The broader opportunity is to combine such operations across products without rebuilding the application's mechanics for every new workflow.
Compare the second workflow as well as the first
The bigger opportunity is to give agents a growing, reusable understanding of the systems they can operate. Many businesses have connected a subset of their applications while leaving the difficult internal and legacy steps with people. Monarch discovers and verifies those steps, then orchestrates workflows across the resulting map.
Mapping a new product typically takes hours; building another workflow over mapped operations can take minutes using inference. Those are technical creation times. Authorised access, application complexity, and acceptance testing still shape the full delivery schedule.
Keep Claude or OpenAI as the intelligence in the design. Monarch supplies maintained application knowledge and the means to coordinate its actions. A delivery-change pattern can then become the starting point for a different service workflow, with rules adapted to that business and a fresh test of the outcome. Your team does not have to rediscover the same application for every agent it builds.
Faster execution needs a fair test
A reusable programmatic operation can avoid repeated screenshot interpretation and individual interface decisions. That is the reason to investigate operating closer to the application layer.
Direct builds can also use APIs, scripts, and batched operations. Neither approach wins a speed comparison from its category name alone. The computer-use comparison explains the execution routes in more detail.
Test the same model, request, records, permissions, and completion criteria. Include discovery or implementation time separately from repeat-run execution. Report the cost of retries and operator intervention alongside model and infrastructure costs.
For larger workflows, preserve dependencies. Profile and order lookups may run together after identity is established; a consequential write must wait for its prerequisites and approval. Parallel work earns its place when it reduces elapsed time without changing the required result.
Maintenance is part of the purchase
A demonstration proves a particular run worked. Change the fulfilment application and the comparison becomes more useful.
Move a field, alter a required step, or change a permitted operation in a test environment. Then examine whether the workflow detects the change, adapts its path, checks the result, and stops when it cannot establish that the action is valid.
Monarch's maintenance and self-healing address the discovered application path. They do not authorise a new business policy. If the business changes who may redirect an order, the process owner still needs to approve and implement that change.
| Test case | Evidence to collect |
|---|---|
| Both updates succeed | Correct customer and order records, plus the linked service case |
| Approval arrives after dispatch | The changed business state is recognised and the request follows the exception process |
| Submission succeeds but the response is lost | The workflow checks the order before repeating the write |
| Application behaviour changes | Detection, repair, or escalation, followed by verification of the intended result |
| A person must intervene | The unresolved work is clear and human effort is included in the operating cost |
Use these results to compare cost per completed workflow. A low model bill can sit beside a substantial integration-maintenance bill.
Make the decision around the work you need next
Build directly when the required operations are covered, your engineers can own the implementation, and the ongoing effort is acceptable. Keep integrations that already perform the job reliably.
Evaluate Monarch when application discovery, missing actions, and recurring maintenance constrain the workflows you can bring into the business. Start with the difficult step in a real process and prove the complete outcome around it.
If your decision includes an existing cloud or automation estate, compare the specific roles of Microsoft's Copilot, Foundry and Fabric stack, Google's Gemini stack and Workato. Each can supply more than a model, and each deserves an evaluation against its actual capabilities.
Bring one cross-system workflow to a Monarch discussion. We will identify the operations already covered, the application work still required, and the result the pilot needs to prove.