Ten AI assistants for UX work, with human evidence at the center

Build capacity around a real job

Organize AI help around research preparation, exploration, prototyping, and review while keeping design decisions grounded in real people and context. The aim is to make a testable flow with traceable design decisions. Ten assistants means ten clearly defined roles, not a requirement to buy ten accounts or run ten agents at once. Several roles can begin as separate conversations in one tool. Add automation only after you understand the job and can recognize an acceptable result.

Ten roles to adapt to your work

1. Brief clarifier. Separate the user task, business goal, constraints, and unknowns.
2. Research planner. Draft neutral study questions and a method for human review.
3. Evidence organizer. Group permitted notes while preserving source IDs and contradictions.
4. Journey partner. Map observed steps and clearly label assumptions.
5. Exploration partner. Propose distinct approaches with tradeoffs and unanswered questions.
6. Content partner. Draft interface language matched to the task and product facts.
7. Prototype builder. Implement a bounded interaction with relevant states.
8. Critique partner. Identify potential usability issues and explain what evidence is needed.
9. Handoff assistant. Document behavior, states, dependencies, and acceptance criteria.
10. Decision librarian. Keep the rationale, evidence, rejected options, and next test together.

Give each role a working agreement

Specify the inputs it may use, the output format, the quality bar, the tools it needs, and where it must stop for your decision. Keep an approved project brief and source folder shared across the workflow. Begin with draft-only or read-only tasks; review a few examples before allowing changes. An assistant needs enough context to help, but not access to every file or account you own.

Practice one handoff before building a team

Start with the first two roles on a real example. Review the output before passing it onward. Ask the second role to flag missing evidence and contradictions rather than quietly filling them in. Keep a short decision record alongside the artifact. If you are correcting the same error repeatedly, improve the brief, examples, or checklist before adding another assistant.

A starting prompt

Using this task brief and anonymized evidence, propose three interaction approaches. Cite the evidence behind your reasoning. Include empty, loading, error, and recovery states. Distinguish observations from hypotheses and list what must be tested with people. Do not fabricate research.

What would “20×” actually mean?

Define the unit first: for this workflow, it could be a reviewed prototype variant. In a hypothetical comparison, 200 minutes of manual work divided by 10 minutes of total AI-assisted work would be 20× faster for that task. The assisted time must include briefing, review, corrections, and an appropriate share of setup. Both results must meet the same acceptance standard. This is an illustration of the calculation, not a result we have measured or a promise about your work.

Measure the whole system

Record at least a few comparable runs before making a performance claim. Track accepted output, total time, tool costs, failures, and rework. Large gains, including more than 20× on a narrow repeatable task, are mathematically possible, but the size and reliability must be demonstrated. Ten simultaneous drafts do not make the business ten times more effective if you spend the next day fixing them. Revenue, customer outcomes, and hours saved are different measures.

Choose tools by the job

• ChatGPT Work: multi-step work with connected context and tools.
• Claude’s Cowork capabilities: help with multi-step knowledge work and files.
• Claude Code: coding tasks with specialized subagents.
• Grok Bot: persistent assistants working across connected tools.
• OpenClaw: an advanced option for separately configured agents and routing.

Access and limits depend on the product and plan. OpenClaw also involves configuration, maintenance, and model costs. You do not need every option. Choose one that fits your current task and learn to review its work before expanding.

Your first-week experiment

Choose one recurring task and capture how you currently do it. Set up the two roles that remove the most repeated effort. Use them on a real piece of work, inspect the result, and record total time and changes needed. Keep the roles that help you produce acceptable work; revise or remove the ones that add overhead. The exciting part is gaining the confidence and capacity to attempt useful work you previously postponed.

Learn the process by building something small

In Two Mondays, Ashley will build a product and show the steps from validation and planning through design, coding, testing, and marketing. Bring a small project and practice the workflow. The workshop meets October 26 and November 2, 2026, from 7–9 p.m. Eastern, on Zoom with a West Palm Beach in-person option. The $99 ticket covers both Mondays and optional setup help and office hours. Paid software and hosting are separate; you can follow the demonstrations without buying every tool mentioned here.

Use the free UX resources

Official tool references

• ChatGPT Work overview
• Claude Cowork overview
• Claude Code subagents
• Grok Bot overview
• OpenClaw multi-agent routing

Tool descriptions checked October 10, 2026. Interfaces, plans, limits, and availability can change. Examples and exercises in this article are original teaching scenarios, not measured client results.

Keep exploring

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How to prioritize UX friction without giving your product a made-up score