I studied Human-Computer Interaction and Communication Design before AI became the interface.
Back then, the questions were:
- How do humans understand what a computational system can do?
- How do they recover when it fails?
- How do they build trust without losing control?
- How does an interface shape behavior?
More than a decade later, I’m realizing these are still the core questions in agent design.
- Can the user understand what the agent system can do?
- Can they tell what it just did?
- Can they recover when it gets something wrong?
- Can they stay in control without doing all the work manually?
- Does the system reduce cognitive load, or does it create a new kind of review debt?
Most of these questions are not model capability problems. Agent systems are probabilistic, but we still have decades of HCI work to learn from.
For example, Donald Norman’s core model separates two failures:
1. The Gulf of Execution, where the user cannot easily translate intention into system action.
2. The Gulf of Evaluation, where the user cannot easily interpret what the system did or whether the goal was met.
For agents, this becomes:
- Execution gulf: how do I tell the agent what I actually want, at the right level of abstraction?
- Evaluation gulf: how do I know what the agent did, why it did it, what it changed, and whether I should trust it?
Nielsen’s 10 usability heuristics are another example. They have been a standard because they describe durable human-machine mismatches.
For AI systems, we can translate them like this:
1. Visibility of system status: show the agent’s phase, confidence, pending actions, blocked assumptions, and irreversible steps.
2. User control and freedom: every AI action needs undo, edit, reject, regenerate, or approval.
3. Error prevention: catch bad context, stale memory, missing tools, and wrong template or project scope before generation.
4. Recognition over recall: do not make users remember prompt syntax, hidden tool names, or implicit state.
5. Helping users recover from errors: treat wrong AI output as a normal product state, not an exception.
One of the most interesting readings we had back in university was “Distributed Cognition: Toward a New Foundation for Human-Computer Interaction Research.”
They argued that HCI should study cognition distributed across people, artifacts, representations, and environments, not only what happens inside one user’s head.
This turned out to be the theoretical backbone of my Digital Brain project. digitalOS is not just a markdown note repository. It is closer to an external cognitive architecture: identity files, logs, memories, drafts, research notes, tools, and agents coordinating to produce judgment.
Agents are not just backend systems.
They communicate state, intent, uncertainty, capability, and failure.
If that communication is bad, the system feels unreliable even when the model is strong.
The practical framework I’m using more often:
- Can the user see the agent’s current state?
- Can they inspect the context behind the output?
- Can they correct the system without starting over?
- Can they undo or reject actions?
- Does the agent know when confidence is low?
- Does memory improve the system without making it unpredictable?
- Does the workflow fit the human’s actual work?
The more I build AI systems, the more I come back to old HCI lessons.
Context and harness engineering are not just about feeding better information into models.
We are designing the whole human-machine system around them.
I never thought I would open IxDF literature articles again after graduation, but here we are, revisiting the old lessons.
IMHO the number of AI products that offer this kind of UX is still limited.
Terminals are not the answer for most users. They are powerful, but they also constrain the interaction model heavily.
Cursor is one of the clearest examples for me right now. Especially with the new Agent interface, you can see many of these lessons embedded into the product.
That is probably the direction agent products need to move toward.
I’m also trying to find more people working at the intersection of HCI and AI.
If you know researchers, designers, builders, or papers I should follow, please tag them.
I’m rebuilding this part of my thinking and want to read from people who have been working on this.
We do not just need more capable models.
AGI needs better human-machine systems.