From a device document to a qualified IoT driver

AI-generated IoT driver code is a candidate implementation, not evidence that a component is supported. Before you treat it as supported — in your own operating documentation or to a customer — you need an evidence chain that connects the source documentation to observed behaviour on the exact target hardware. That distinction matters whether a person or an agent writes the first version.

Start from the documented component

Identify the component revision, its interface, the intended operations and the operating conditions. Supply the documentation and define what the driver must not do: permitted actions, safe states, and who is responsible for physical testing. Establishing the exact electrical identity, interface behaviour, wiring and control consequence is what makes an integration verifiable at all — and it is also how you discover early that some devices will not be supportable. Do not ask a model to fill missing electrical or command facts from plausibility.

Test beyond the happy path

Driver code can behave correctly in normal operation and still fail on corner cases such as resource shortages or bus errors; robustness testing with fault injection is a recognised stage of driver development for exactly that reason (ISSTA 2015). Embedded development guides treat error behaviour as something to manage systematically, not only nominal operation (Texas Instruments). An acceptance plan should cover normal readings or actions, invalid input, unavailable equipment, interrupted communication, restart, and what an acknowledgement actually means — selected for the actual risk and interface rather than copied as a universal checklist.

Simulation helps examine software behaviour, but it does not establish how the real device behaves. Hardware-in-the-loop testing exists because simulated environments cannot replicate real-world interface settings, timing and wiring (MathWorks), and it checks the software on the electronics it will actually run on before deployment (Ansys/Synopsys). Tests involving physical outputs require suitable isolation, explicit permission and qualified supervision.

Retain the result, not only the code

A useful acceptance packet records the code version, the test configuration, the checks performed, the observed results and the remaining limitations. A record that "the test ran" is not the same as a record that the required behaviour was observed. An illustrative packet might read: measurement checks passed on one documented component revision, while restart behaviour remains unresolved. That is useful partial evidence — and it is not support for every version of the component.

The same review discipline applies to generated code generally: run automated tests and static analysis first, verify context and intent, and ask which functional tests are still missing before trusting the change (GitHub Docs).

Promote support deliberately

Code review, hardware evidence and operational acceptance are separate gates. Decide which configurations are supported and who maintains the integration when documentation or dependencies change. The productive question is therefore not "Can AI write this driver?" but "What evidence will let us trust and maintain this integration?" That question makes generated code part of an engineering process instead of a shortcut around one.

How Manna runs this process

This is also how we work with MIND AIoT: a new component is specified with documentation, attached to one test node, and tested there under explicit test permission; the agent's reported code and results are reviewed and accepted before anything is deployed to production, and the documentation-to-driver workflow retains the tested driver in a project library.

If you are deciding when generated driver code is ready to be treated as supported, contact Manna to discuss your equipment and the evidence you need.

Prepared with AI assistance for Manna Insect Oy.