Human-in-the-loop
Humans approve material decisions, exceptions, and releases. The agent prepares work; it does not replace the owner.
Governance & Validation
Built for oversight, evidence, and controlled execution.
USDM treats governance as the operating system for agentic workflows: intended use, source boundaries, human approval gates, evidence capture, validation scope, and change control all stay visible.
Core controls
The eight questions that matter before go-live.
If a workflow touches regulated records or important business decisions, these are the controls that keep it defensible.
Audit trails & evidence
Prompts, retrieved sources, outputs, and reviewer actions are captured so the workflow is inspectable later.
Risk-based validation
Validation scope follows intended use, source systems, human decision points, impact, and control criticality instead of trying to validate the universe.
GxP / Part 11-aligned handoffs
Where applicable, access control, record provenance, reviewer attribution, e-signature handoff, audit trail capture, and controlled retention are designed into the workflow from the start.
Data boundaries
Agents only see approved sources and only act inside defined queues, systems, and permission sets.
Monitoring & oversight
Teams watch output quality, exceptions, and drift so the system stays useful after launch.
Change control
Updates to prompts, models, logic, or sources follow documented change control and review.
Deployment readiness
Go-live requires owners, test evidence, rollback paths, training, and clear operational support.
How USDM validates
Right-sized validation, not theater.
The validation strategy should match the intended use, risk profile, data sources, human decision points, and control criticality. More scope is not automatically better; clearer scope usually is.
Define the intended use and boundaries before writing the test plan.
Validate the specific workflow, source systems, and approval path that will be used.
Document prompts, rules, evidence sources, and fallback handling.
Test the failure modes that matter: bad data, missing approvals, and stale sources.
Keep a change log so model, prompt, or source updates stay under control.
What good looks like
A system that can be reviewed after the fact.
The real test is whether someone can reconstruct the intended use, what the agent saw, what it produced, which source records were used, who reviewed it, what changed, and why the final decision was acceptable.
Deployment readiness checklist
Named business and technical owners
Evidence package and test results
Rollback / fallback path
Monitoring and issue triage process
Training for the people who will actually use it
Change control for future updates
Next step
Bring one use case through the full control stack.
USDM can help define the validation scope, document the controls, and turn the governance story into something your teams can actually use.
Talk to an agentic AI specialist
Design the governed operating model your team needs.
USDM can help define the workflow, the validation scope, and the oversight controls — so the first use case is worth automating and audit-ready from day one.
- Agentic team design scoped to your domain and regulatory context
- GxP-validated AI agents with human-in-the-loop controls
- Audit trails from prompt to approval for every agent action
- Operates inside your existing systems: QMS, Veeva, ServiceNow, ERP