The real product in AI age isn’t just what we build, it’s how we design judgment, delegation, and trust
AI DESIGN
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If you can't prove your work's value, you'll always be seen as a cost ...
not a strategic partner!
@DesignWaves
6 min read
If you can’t measure impact, can you really prove value?
Designers, we are problem-solvers at heart.
But in a world obsessed with ROI and speed, good design alone isn’t enough. And problem-solving doesn't cut it, if we're solving the wrong problem. The harsh truth? If we can’t measure our impact, we can’t prove our value.
We’ve seen brilliant designers struggle to articulate their worth because often we're tracking the wrong metrics, whether because someone on top decided "they know better", an opportunity to influence failed through the cracks, or even maybe we were never taught how to play at the business level.
Often typical metrics set in the team tend to focus on outputs (“we shipped 10 features!”) or vanity metrics (“our app has 1M downloads!”) while the real value - outcomes - goes unmeasured.
What excites me is this shift is that UX research, service flows, and systems logic aren’t just insights anymore … they’re bound to become inputs to agent training.We’re moving into a world where our design work doesn’t just influence user behaviour, but it helps shape machine behaviour too. And that’s a huge responsibility.
So the question I’m sitting with lately is:
How do we design delegation?
Not just for efficiency, but for care. For trust. For resilience.
Because in AI-powered systems, the invisible parts we design - ethics, oversight, escalation paths - will inevitably end up being the most important features of all.
And now, with the rise of AI agents — tools that can plan, reason, and act autonomously — we’re entering a whole new frontier:
We’re not just designing for people anymore.
We’re designing how machines behave.
Delegation is no longer just a management skill.
In the age of AI agents, it’s becoming one of the most important design challenges of our time.
Because when machines start taking action on our behalf - responding to emails, diagnosing health risks, summarising legal documents, or deciding what gets flagged or ignored -the core design question becomes:
👉 What should we delegate?
👉 To whom?
👉 And how do we stay in the loop when it matters most?
This is designing delegation — and it’s the foundation of any system that blends autonomy and intelligence with human oversight.
1. Clarify the purpose of the agent
Before anything else: why does this agent exist?
Is it a co-pilot helping someone move faster?
Or is it an autopilot taking full control, with optional oversight?
Is it reducing repetitive tasks?
Is it making judgment calls, triaging, or coordinating actions with others?
💡think of: What is this agent relieving the human from doing, and what is it not allowed to touch?
2. Define the knowledge boundaries and feedback loops
Delegation doesn’t mean disappearing the human. It means strategically placing humans in the loop:
For review
For correction
For escalation
Or simply for awareness
Some decisions should never be fully automated. Others can be — but with traceability and override built in. So embedding human-in-the-loop by design, will help you from the get go evolve the existing experiences people have with your service or product..
💡think of: At what point does the human need to re-enter the flow? And how will the system invite them back in - without overwhelming or confusing them?
Every delegation system starts with assumptions.
What does the agent know (data sources, history, rules)?
What is it assuming about the user, the context, the goal?
What does it need to ask before acting?
This is where NLU, research, and domain expertise intersect. Designers can help build better agents by feeding in real workflows, edge cases, and nuanced user intent.
💡think of: What assumptions are unsafe, biased, or invisible? What should trigger a re-check or escalation?
Make the delegation explainable as much as tech limitation permit.
Delegation isn’t effective if the person doesn’t trust what the agent did — or why.
That’s where explainability comes in.
Show what the agent did, what tools it used, what data informed its decision — and where uncertainty or exceptions were flagged.
Even better: let the user ask why at any point.
💡think of: What does the user need to know to feel safe handing off this task? What language builds trust without drowning them in complexity?
Design for feedback loops.
Delegation should never be a one-way street.
Design systems that let people:
Praise helpful actions
Flag weird results
Teach the agent what they really meant
This isn’t just about usability — it’s about long-term learning and safety.
💡think of: How can the user shape the agent over time? What patterns can be tuned without technical friction?
While most people think of feedback as a form or a survey, when we’re designing delegation for AI agents, feedback isn’t an afterthought, it’s the only way agents evolve responsibly.
Without it, agents keep guessing. And when they guess wrong in high-stakes systems, the cost isn’t just a bad experience - it’s mistrust, harm, or systemic bias.
Feedback in AI agent systems serves three critical functions:
Correction - Helping the agent do better next time
Learning - Enabling the system to adapt across contexts or domains
Accountability - Creating traceability and auditability
When designing for AI agents, it’s no longer enough to ask “Did the user complete the task?”
Now, we have to ask:
Did the agent act as intended?
Was the outcome safe and useful?
And what did the system learn from that moment?
Feedback isn’t just a UX nice-to-have … it’s the mechanism that makes agents more aligned, adaptive, and responsible.
Through my work designing high-stakes systems - from BioTech to AI-assisted workflows - I found myself asking: How do we design feedback that doesn’t just respond, but evolves?
So I developed a simple, scalable model I call the G.R.A.I.N. Loop
G = Ground → Anchor the agent’s action in visible context.
What did the agent just do? What data or reasoning path was used?R = Reflect → Invite human input on success, trust, clarity.
How did this feel? Was the outcome expected or surprising?A = Adjust → Allow in-the-moment correction or override.
Can the user change something now? Can they undo, pause, or redirect the agent?I = Integrate → Feed input into learning, fine-tuning, or pattern adjustments.
Does the system update its behaviour next time? How is that recorded or verified?N = Notify →| Show the impact of that feedback.
Was the user heard? What changed, if anything? When and how will it show up again?
Designing feedback this way helps systems learn responsibly while keeping humans in control.
It turns passive users into active participants in shaping intelligent behaviour — and shifts AI design from one-time interactions into evolving relationships.
3. Set ethical guardrails.
Delegation in high-stakes environments (healthcare, finance, justice) needs ethical clarity.
You’re not just designing a tool —
You’re designing what the tool is allowed to do.
Things to consider:
What must always involve a human?
How are consent, privacy, and equity considered?
What happens when things go wrong?
💡think of: What harm can occur if the agent gets it wrong, and how do we prevent that?
This is the age where the anti-personas and anti-archetypes come back to play.
Last but not least, design for teams, not just individuals.
In multi-agent systems, delegation becomes a team coordination problem.
You need to design:
Who leads
Who observes
Who approves
And how agents hand off to each other or back to the human
Think less like “interface design” and more like orchestration of intelligent roles.
💡think of: How do we create clarity, accountability, and harmony in agent teams — just like we would in human teams?
This is the age where the anti-personas and anti-archetypes come back to play.
Delegation isn’t about removing the human.
It’s about respecting the human’s time, trust, and boundaries.
In an AI-powered future, our job as designers is not just to create smooth experiences —
It’s to shape intelligent systems that behave ethically, transparently, and in service of people.
That means designing:
What agents do
What they don’t do
And how humans stay in control of what matters most
That’s not just a design pattern.
That’s a responsibility.