Supervised AI Workflow Operating Model
A versioned, documentation-first operating model for supervising AI-assisted project delivery from discovery and scope through implementation, evaluation, validation, human review, rollback, handoff, and closure.
The problem
AI-assisted work is hard to review without operating rules.
When scope, sources, permissions, evaluation, technical validation, and approval live in different places, a polished output can hide missing evidence or unclear authority.
What Christian needed to solve
Create one lifecycle that makes intent, evidence, risk, decisions, contribution, ownership, rollback, and completion observable.
What Christian designed
A documentation-first control model.
The model defines discovery, problem definition, prioritization, scope, execution briefs, bounded building, output evaluation, technical validation, accountable human review, handoff, closure, and governed pattern reuse.
How it works
Each stage has inputs, required activities, outputs, approval owners, stop conditions, and an exit handoff. Sensitive, external, destructive, irreversible, or public actions require explicit human approval.
Contribution
Christian’s role
Christian identified workflow friction, defined objectives and operating boundaries, established acceptance criteria, directed AI-assisted implementation, reviewed outputs, requested revisions, diagnosed system-level failures, made scope and approval decisions, validated outcomes, and directed handoffs.
AI and tools
Implementation support
AI tools consolidated verified patterns, drafted standardized artifacts, implemented the validator and preview, and executed bounded validation under Christian’s direction. The model records human and AI contribution separately.
Evidence and validation
A complete synthetic example exercises the model.
The 93% figure is the synthetic example’s final project-level evaluation—not a model-accuracy score or scientific benchmark.
The automated operating-model validator passed, including 3/3 validator tests. Chrome acceptance checks at desktop, tablet, and mobile widths found no console errors, failed resources, horizontal overflow, or unexpected external destinations. These checks are not formal WCAG certification.
Key decisions and tradeoffs
- Documentation-first controls instead of orchestration software
- One authoritative lifecycle instead of loosely connected checklists
- Allowlisted actions, named approvals, and explicit stops
- Separate evaluation, technical validation, and human decision records
- Rollback readiness and handoff as completion requirements
Tools and methods
- Authoritative lifecycle and 21 structured templates
- Project-level evaluation and traceability records
- Automated consistency validator and validator tests
- Dependency-free local browser preview and accessible SVG diagrams
- Versioned change control and documented closeout
Known limitations
This is documentation and local evidence, not orchestration software. The example is synthetic. There is no production-use, client-use, adoption, certification, or ROI claim. Automated scans are bounded, browser review is not formal WCAG certification, and historical projects did not uniformly follow version 1.0.
What Christian learned
Teams can review AI-assisted delivery more clearly when scope, sources, prohibited actions, evaluation, validation, approval, and handoff remain distinct but connected.
What could improve next
Apply the model prospectively to additional bounded internal projects, then revise only the patterns supported by recorded evidence.