Make powerful agentic work useful without hiding decisions or removing human accountability.
Self-initiated case study · July 2026
Designing human accountability into agentic work.
A concept exploring how governed AI agents could support regulated enterprise work through visible plans, traceable evidence, safe failure and human approval.
Concept lead, product designer, systems designer and hands-on prototyper.
Six responsive routes, eight deterministic scenarios and twenty governed AI patterns.
Local event transitions, fictional enterprise data and no live LLM or production integration.
Linda’s AI design framework
Five principles for governed enterprise agents
The framework turns responsible-AI intentions into observable product behaviour.
Agency must be visible
People should be able to see, edit, pause and stop an agent’s plan. Required controls remain visibly protected.
Consequential actions require human control
Sending, exporting and changing regulated wording always require an explicit, qualified human decision.
Claims must be traceable to evidence
Every material sentence connects to an approved source, owner, version and region using qualitative evidence strength.
Failure must preserve progress
A conflict, tool outage or privacy boundary stops only unsafe work and keeps completed steps available.
AI patterns must be measurable and revisable
Evaluation data reveals where patterns should be revised, tested or retired rather than treated as finished UI.
Three-minute walkthrough
How to present the concept
“How do we allow an AI agent to complete useful work without obscuring what it is doing or removing human accountability?”
- 01Task and agent plan
Show that the plan is editable and stoppable while mandatory compliance checks remain protected.
- 02Draft and evidence mapping
Select a claim and connect it to the exact source, owner, version and region.
- 03Conflict or privacy boundary
Demonstrate that the agent stops affected work, preserves progress and escalates the decision.
- 04Pattern Library
Explain that these behaviours are reusable patterns with content, accessibility and implementation rules.
- 05Evaluations
End with how patterns are measured, revised or retired using real performance signals.
What this demonstrates
Strategy made tangible through an interactive system.
The prototype demonstrates product thinking, responsible-AI literacy, system design, accessibility, hands-on execution, technical awareness and executive storytelling. It does not claim to be a validated clinical system or a deployed global AI programme.