RPA vendors position their products as the no-code solution to enterprise integration. If your systems have APIs, use RPA to connect them without writing code. The pitch is compelling: non-technical users can configure integrations, development time is zero, and the vendor handles maintenance.
The reality is more complicated. RPA tools make some integrations accessible that would otherwise require engineering resources. They also introduce reliability and maintainability problems that don't surface until the integration is in production. This article isn't a polemic against RPA — it's an attempt to map where each approach is the right choice.
The RPA Promise
Robotic Process Automation tools (UiPath, Automation Anywhere, Microsoft Power Automate, and similar) originally automated UI interactions — screen scraping, mouse clicks, form filling. Modern enterprise RPA tools extend this to API integrations via pre-built connectors and visual workflow designers. You configure a trigger (new record in CRM), one or more actions (create record in ERP), and the platform handles the execution.
For simple, high-level workflows — sync a contact created in one system to another, send a notification when a shipment status changes — RPA tools are genuinely effective. The configuration is accessible to non-engineers, the time-to-running is fast, and the maintenance burden is low as long as the upstream APIs don't change.
RPA in Practice
The failure modes in production RPA integrations cluster around three problems:
Reliability under error conditions. RPA workflows are designed around the happy path. When the API returns a 429 because you've exceeded the rate limit, or a 500 because the upstream is briefly unavailable, RPA platforms vary widely in how they handle it. Some have basic retry; others fail the workflow run and require manual restart. For integrations that run continuously — syncing order status, processing inbound webhooks — a workflow that fails silently and requires manual restart is operationally costly.
Pagination handling. Most RPA connectors for enterprise APIs handle simple list endpoints that return all records in one response. Cursor-based pagination across thousands of records — the typical requirement for initial data syncs — is either unsupported or requires custom scripting that defeats the no-code premise. If your integration involves more than a few hundred records, you're likely writing code anyway.
Schema change sensitivity. When the upstream API adds a required field, renames a field, or changes an enum value, RPA workflows that depend on that field break silently or with unhelpful error messages. Debugging requires navigating the RPA platform's execution logs rather than standard application logs. For teams that don't own the RPA platform (common in enterprises where integration tooling is owned by IT operations rather than engineering), getting visibility into a failed workflow run can take days.
The SDK Approach
SDK-based integrations are code. They run in your infrastructure (or a hosted execution environment you control), they emit logs to your logging system, they're testable, and they're deployed via your existing deployment pipeline. When something goes wrong, you debug it the same way you debug any other application code.
The cost is that they require an engineer to write and maintain them. This is not a small cost — it's exactly the cost that RPA promises to eliminate. But for integrations that have complex logic, operate at high volume, involve multi-step pagination, or are operationally critical (a failed order sync is a lost order), the engineering investment pays for itself in reliability and debuggability.
Reliability Comparison
The most useful way to compare the two approaches is by failure modes:
- Transient API errors (5xx, network timeouts): SDK with retry logic handles automatically. RPA behavior depends on platform and configuration; often requires manual restart.
- Rate limiting (429): SDK reads
Retry-Afterand waits correctly. RPA platforms vary; many don't honorRetry-Afterheaders. - Pagination across large datasets: SDK handles natively via cursor/offset/page-token strategy. RPA typically requires custom scripting for datasets over a few hundred records.
- Token expiry mid-job: SDK refreshes proactively. RPA workflows may fail at the first 401 if the auth module doesn't handle refresh.
- Schema change in upstream API: SDK fails fast with a typed error that surfaces in your logs. RPA may fail silently or with an obscure platform-specific error message.
When to Use Each
Use RPA for: simple trigger-action workflows between well-supported connectors, non-technical users who need to configure integrations without engineering involvement, low-volume syncs where occasional failures are tolerable and manually restartable.
Use an SDK for: integrations with operational SLAs, high-volume data syncs with pagination, multi-connector workflows with complex logic, integrations that need to run in your CI/CD pipeline with tests, and any integration where silent failure is unacceptable.
These are not mutually exclusive. Large enterprises often use both: RPA for the long tail of simple integrations owned by business teams, SDK-based integrations for operationally critical data pipelines owned by engineering. The mistake is using RPA for the second category because "it was faster to set up."