The same three deficiencies, addressed across the AI stack
What CODITECT does for code, applied to every AI use case in the company.
The same three deficiencies extend to every AI-touched workflow - ERP, CRM, HR, claims, underwriting - not only software. None has a system to select the right tests, run them, and capture and validate the test results. The AI Operations Layer addresses all three across every AI-driven process.
"We and every other software company in the world are outstripping our ability to test what we're building."
Why now: the velocity of agentic coding has decoupled from the velocity of testing, auditing, and validation - the knowledge and proof that AI agents did what they were tasked to perform, i.e. testing, in this case. An AI agent can produce more code in a day than a team used to write in a sprint. The test, audit, and compliance layers did not get faster at the same rate. The gap is structural and widens with every model release.
Three deficiencies - in every company today - that no software addresses:
- determining which tests need to run for a particular release
- checking whether they ran
- recording the outcome
Mark Walker, nue.io - meeting transcript [00:46:36]
The AI Operations Layer is the company-wide expression of addressing the same three deficiencies, applied across every AI use case, not only software.
The same three deficiencies exist across the AI stack
The deficiencies Mark named in software (which tests, that they ran, the outcome recorded) apply to every AI-touched workflow in the company. ERP automations, CRM agents, claims triage, underwriting, marketing copy generation - none have a system that selects which tests apply, runs them, and records the outcome.
The AI Operations Layer is the same primitives extended above the application stack.
Outcomes by stakeholder
- CEO - AI adoption across teams stays predictable. Board-level reporting is one query, not a quarterly project.
- CISO - data flow into and out of every AI model is recorded; which model saw which data is queryable; the audit posture is current at all times.
- CTO - the foundation model can change without rewriting the work; routing is per task, recorded, reversible.
- CFO - every AI call ties to a task, a cost, and an outcome; ROI is calculable from the same audit trail that satisfies compliance.
- Risk and compliance lead - the regulatory framework (NAIC, NYDFS, NIST AI RMF, EU AI Act, sector-specific) is mapped to controls; compliance evidence is produced as a byproduct of normal work.
Compliance and risk as the operating posture
Every change recorded includes the approving person, the time, the model used (if any), the data the model saw, the tests run, and the outcome. The same record satisfies an internal audit, a regulator examination, and a quarterly risk review.
Failed tests and dependency vulnerabilities open tracked issues automatically through the CI hooks. Model-drift detection and regulatory-change feeds are part of the Universal Quality Development Harness 9-phase loop (DISCOVER, ANALYZE, PLAN, SPECIFY, RESEARCH, IMPLEMENT, REMEDIATE, DEPLOY, OBSERVE) per ADR-320. Closure of any tracked issue requires evidence, not assertion.