As AI takes on more complex data transformation work, the challenge shifts from generating logic to understanding and debugging it. Deterministic AI systems like Doyen’s can automate the migration of millions of financial records. Rarely, however, can these migrations be completed without human intervention in the form of validation/supervision and the addition of custom business logic. These situations are where human-ai interfaces become critical.
At Doyen, we’ve learned that debugging AI-generated data logic requires two complementary perspectives corresponding to two different personas involved in a migration: business visibility and technical depth.
Business User Interfaces: Understanding Outcomes
Business users need to see and understand, from a semantic perspective, what the system did, clearly, quickly, and in their own language.
We focus on making AI-driven transformations explainable in the following ways:
- Global and per-record status views — see which records loaded successfully and which didn’t. Both a global report but also on an individual record lens.
- Record-level mapping inspection — view the full lineage of any record: source → mapped → loaded, including how a given natural-language Business Rule affected it.
- Natural-language reconciliation — ask questions like “Which invoices in Zuora don’t match the totals in NetSuite?”
- Business rule history — browse previous Instructions and see how logic evolved over time in natural language.
- Reversions to previous business rules — roll back to a prior set of Instructions with one click.
- Conversational interface (ongoing development) — an evolving chat layer for exploring data, rules, and anomalies interactively.
These tools make it possible for finance and accounting teams to trust AI-driven automation without needing to read code.
Technical User Interfaces: Understanding Logic
For IT, data engineers, and power users, the needs go deeper.
We expose everything necessary for debugging at the code level:
- Editable generated code — inspect, tweak, and re-run the deterministic Go transformations.
- Code versioning and rollback — review prior revisions of generated logic for full traceability.
- Instruction-level impact analysis — see precisely how code changes or natural-language edits modify record mappings downstream.
This layered transparency ensures that AI never operates as a black box. The business can operate confidently, while engineers retain total control over the underlying logic.
The Future of AI-Human Debugging
As AI systems move closer to core financial workflows, their success will hinge on accuracy and interpretability. At Doyen, we’re building those capabilities.