Most AI coding discussions focus on model capabilities. Our focus is different: governance, provenance, determinism, sustainability and audit-ability.
AssuredCode transforms structured specifications into validated software artifacts through a deterministic-first generation pipeline. The architecture is designed to support enterprise environments where reproducibility, traceability, and compliance are as important as developer productivity.
Key concepts include:
- Specification-driven development
- Normalization
- Provenance tracking and audit-ability
- Deterministic-first generation with optional LLM integration
- No dependency on external code harvesting during generation
- Support for regulated and mission-critical environments
- Low latency and energy usage
Autonomic AI has been exploring these concepts through healthcare and streaming-event use cases, including high-throughput fraud detection architectures where AI operates out-of-band to synthesize governed logic while production systems continue to execute deterministic code paths.
As AI development matures, an interesting question emerges:
Can software generation move beyond prompt engineering toward governed software synthesis? I believe the answer is yes, and AssuredCode is our contribution toward that direction.
I would be interested in hearing how others in the IBM community are approaching software provenance, AI governance, and deterministic code generation.
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John Harby
CEO
Autonomic AI, LLC
Temecula CA
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