AI Systems
Credo AI
As Credo AI expands beyond static workflows, we need a model-independent system that makes AI interactions consistent, explainable, and auditable across workflows. This project created the underlying patterns that now powers AI-driven decisions in the platform.
Lets zoom in to the suggest popover below.
AI reasoning suggests “Coding” and “Project management” because it has access to “Project-GAIA-PRD.pdf” and the Credo AI registry of domains. The suggestion appears only after the AI communicates confidence and meets our reliability thresholds. The final nature of the suggestion is determined by the system based on a set of variables.
Documenting this full reasoning trail not only lets us build guardrails and test cases, but also provides the auditability our enterprise customers require.
This level of traceability becomes even more important as we increase the power and autonomy of the AI. Our initial AI Assist tool is intentionally a guided layer on top of existing workflows, with explicit explanations of what the AI is doing. But as we start allowing the system to decide larger parts of the experience, even small brittleness in the reasoning chain can create large variances and negative outcomes.
Eventually, the AI will be able to determine what information it needs from the user — but only when its reasoning can be made reliable, observable, and safe.
By defining these boundaries (contract, reliability, visibility, and auditability) we built an AI layer that can scale across models, workflows, and teams without rewriting interaction logic. This transforms AI from a collection of features into a stable system foundation for future expansion.