AI-UX — does the human trust it?
Brief 42

Agent Experience Audit

What waiting on an agent feels like — progress honesty, interruptibility, showing the work, and salvaging partial results.

AGENT-UX.md4 phases7 lenses~2.7k chars

Free & open · no signup · read-only — it ends by asking · nothing leaves your machine

What it does

You are working inside this repo. Mission: walk the experience of using an agent-powered feature — kickoff, the wait, the result, the follow-up — and find where the interface hides, stalls, or strands the human.

Read-only pass. Your only write is the report file.

What it looks for

7 lenses it looks through

Phase 2 sweeps the codebase through every one of these, citing file and line for each finding.

1

Progress honesty

a three-minute spinner vs visible steps; progress bars animating on a timer rather than reality

2

Interruptibility

a cancel control exists, is findable, and actually stops the work and the spend — trace what cancel truly does

3

Latency shaping

streaming, early partials, or a skeleton of what's coming vs all-or-nothing delivery

4

Show the work

can the user see what the agent did — steps taken, sources used — or is it a verdict from a black box?

5

Failure UX

the run fails: is the message actionable, are partial results salvaged, is retry one tap or start-over?

6

Input affordances

does the interface teach what the agent can and can't do, or is it an empty box and a prayer?

7

Review before commit

agent-made changes previewable and reversible, or applied the instant the model speaks (see 43)

The deliverable

What lands in your repo

One structured report at the repo root — or in reports/, if you keep one — the same shape every time, ready for a teammate — or the optional Studio — to act on.

AGENT-UX.md
1
Journey walkthrough
state by state, with real timings
2
The worst wait
narrated as the user lives it
3
Findings
each: state · lens · fix · effort
4
The first change
usually visibility or streaming; spelled out
One file. Evidence-backed. It ends by asking before touching anything.
How it works

What it makes the agent do

Every Goal Prompt follows the same four steps, so results are consistent and repeatable — no matter which one you run.

1
Phase 1

Walk the journey

Trace the primary agent feature from the user's seat.

2
Phase 2

Audit through 7 lenses

Progress honesty — a three-minute spinner vs visible steps.

3
Phase 3

Curate

Rank by felt time and felt risk on the most-used flow.

4
Phase 4

Report

Create AGENT-UX.md at repo root.

Get started

Three ways to run this Goal Prompt

01 · COPY

Paste it in

Copy it and paste it into your agent inside the repo you want checked.

02 · INSTALL

As a slash command

Install the goal plugin once — two commands — then just type /goal:agent-experience-audit.

1/plugin marketplace add GhostlyGawd/goal-prompts
2/plugin install goal@goal-prompts

Or install only this Goal Prompt as /goal-agent-experience-audit:

curl -fsSL https://goal-prompts.vercel.app/install | BRIEF=42 sh
03 · AGENT

From an agent (MCP)

Let an agent fetch it mid-conversation, or pull the raw Goal Prompt by URL.

https://goal-prompts.vercel.app/raw/42.md
Transparency

The exact prompt

Nothing hidden — this is the whole Goal Prompt, verbatim. Read it in a minute, edit it, or copy it as-is.

Read the full brief (2,679 characters)
# Goal: Agent Experience Audit

You are working inside this repo. Mission: walk the experience of using an agent-powered feature — kickoff, the wait, the result, the follow-up — and find where the interface hides, stalls, or strands the human.

Read-only pass. Your only write is the report file.

## Phase 1 — Walk the journey
- Trace the primary agent feature from the user's seat: how a run starts, what the screen shows while it works, how results arrive, what happens next.
- Inventory every UI state in code: idle, queued, running, streaming, partial, failed, done. Which exist? Which are the same spinner?
- Measure or estimate the real wait times per state.

## Phase 2 — Audit through 7 lenses
1. **Progress honesty** — a three-minute spinner vs visible steps; progress bars animating on a timer rather than reality
2. **Interruptibility** — a cancel control exists, is findable, and actually stops the work and the spend — trace what cancel truly does
3. **Latency shaping** — streaming, early partials, or a skeleton of what's coming vs all-or-nothing delivery
4. **Show the work** — can the user see what the agent did — steps taken, sources used — or is it a verdict from a black box?
5. **Failure UX** — the run fails: is the message actionable, are partial results salvaged, is retry one tap or start-over?
6. **Input affordances** — does the interface teach what the agent can and can't do, or is it an empty box and a prayer?
7. **Review before commit** — agent-made changes previewable and reversible, or applied the instant the model speaks (see 43)

## Phase 3 — Curate
- Rank by felt time and felt risk on the most-used flow
- Every fix names its state and component; no vibes-level "improve loading"

## Phase 4 — Report
Create `AGENT-UX.md` at repo root:
1. **Journey walkthrough** — state by state, with real timings
2. **The worst wait** — narrated as the user lives it
3. **Findings** — each: state · lens · fix · effort
4. **The first change** — usually visibility or streaming; spelled out

Start the report with today's date. If `AGENT-UX.md` already exists from a previous run, read it first and lead with what changed since.

## Rules
- Perceived speed is a design material; honesty about progress builds more trust than speed itself
- Cancel must mean cancel — work, side effects, and billing
- No AI features with a user surface in this repo? Say so in a one-paragraph null report and stop — a null result is a valid finding.
- If a `reports/` directory exists at the repo root, write the report there instead of the root.
- Before asking, present the top findings as a ranked list in plain words
- Report only — end by asking which fixes to make

House rules for this Goal Prompt

  • Perceived speed is a design material; honesty about progress builds more trust than speed itself
  • Cancel must mean cancel — work, side effects, and billing
  • No AI features with a user surface in this repo? Say so in a one-paragraph null report and stop — a null result is a valid finding.
  • If a reports/ directory exists at the repo root, write the report there instead of the root.
  • Before asking, present the top findings as a ranked list in plain words
  • Report only — end by asking which fixes to make
Keep exploring

More AI-UX briefs