Case study
Agentic Career Workflow
Sanitized public case study demonstrating progression from GenAI assistance to structured AI workflows and a governed agentic operating model.
Role: Product owner, workflow designer, tester, and decision-maker
Problem and context
Turn a multi-stage career workflow into a governed operating model
The project explores progression from GenAI assistance toward a structured workflow in which specialized stages coordinate discovery, evaluation, generation, validation, approval, tracking, and iterative improvement while preserving explicit human decision gates.
Workflow
End-to-end lifecycle
- Discover
- Qualify / Evaluate
- Human Decision
- Generate
- Validate
- Approve
- Track
- Learn / Improve
Agentic properties
Governed orchestration rather than simple prompting
- Persistent workflow state across stages.
- Specialized functional stages.
- Generalized tool use and orchestration.
- Conditional routing based on workflow state and validation results.
- Explicit human decision and approval gates.
- Validation before downstream actions.
- Traceability and governed change control.
- Exception handling and recovery paths.
- Iterative improvement informed by results and observed failure modes.
Governance
Human approval remains explicit
The public case study intentionally excludes private application records, recruiter communications, private prompts and source files, credentials, tokens, and sensitive implementation details. It also does not claim that Andrew personally coded every agent or automation capability.
Related expertise
Continue
Return to the profile, browse all project evidence, review the public resume, or contact Andrew.