karan@embedded:~/ai$
karan@embedded:~$ cat ai-native-workflow.md

Engineering-First, AI-Assisted

Spec-driven, documented, validation-first embedded development — with AI used as an engineering assistant for planning, review, documentation, and workflow acceleration. I'm an embedded engineer first: AI improves engineering discipline, it does not replace understanding of timing, memory, hardware constraints, RTOS behavior, and failure modes.

Spec-driven developmentArchitectureDesign decisionsTDDImplementationSimulationHardware-in-the-Loop validationML validationVerificationDebuggingOperationsEngineering reporting

Agentic engineering organization

A project where AI agents operate like a structured embedded engineering team:

  • Principal manager — defines the goal & success criteria
  • Team leads — break the work into phases
  • Implementation agents — build features
  • Test agents — validate behavior
  • Review agents — check correctness
  • Reporting agents — summarize progress, risks & blockers

Beyond code generation

The goal is to support the engineering discipline around embedded development:

Requirements traceabilityInterface contractsTest coverageHIL proceduresValidation evidenceDebugging playbooksOperational feedback loopsDocs aligned with code

Example in practice: my BLE environmental sensor node was built spec-first with agent-buildable documentation.

AI-native embedded engineering means combining AI-assisted execution with real embedded engineering judgment — timing, memory, hardware constraints, RTOS behavior, drivers, communication interfaces, failure modes, verification, and system validation. The ideas and the judgment stay human.