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Aero Interactive

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Agentic product design for teams shipping AI agents

Agentic product design is the practice of designing software in which AI agents take actions on a person's behalf, so the core design work moves from helping someone do a task to helping them delegate it, verify it and correct it. Aero Interactive designs that layer, the approvals, visible reasoning, error recovery and system rules, for B2B product teams putting agents into production.

Who it is for

Product and design leaders at B2B software companies putting AI agents into products their customers rely on, especially where a wrong action is costly: finance, health, media operations and internal tools.

What changes when people delegate instead of doing

For decades, software design asked one question: how do I help this person complete the task? Once an agent does the task, the question becomes: how does this person know it was done well, and how fast can they fix it when it was not? The person is still accountable for the outcome. The product has to make that outcome legible at a glance.

Teams that design for that shift ship agents people keep using. Teams that bolt an agent onto screens built for manual work get one of two outcomes: an agent that interrupts so often it gets rubber-stamped, or one that acts invisibly until the first expensive mistake. Our longer argument is in [Agentic products live or die at the approval step](/blog-posts/agentic-products-live-or-die-at-the-approval-step).

The four layers we design

Every agentic product we work on is designed across the same four layers. Each one maps to a failure we see in shipped products.

  • Delegation: what the agent may do, scoped by action and by risk, and what it must hand back to a person
  • Verification: review and approval ranked by consequence, with the agent's reasoning and the exact change shown before anything irreversible happens
  • Recovery: uncertainty signals, failure states, undo and handoff, designed as first-class flows rather than error toasts
  • Consistency: rules that keep agent-generated output on your design system, brand and tone, so the product still feels like one product

Where agentic products usually break

Four patterns account for most of the agentic features we are asked to rescue. Each has a design fix.

  • Approval fatigue: every action asks for sign-off, so none of them get read. Fix: consequence-ranked gates. See [When users trust your AI too much](/blog-posts/ai-trust-calibration).
  • Invisible action: the agent did something and nobody can tell what. Fix: an action history people can scan and reverse.
  • Confident wrong answers: output looks equally certain whether it is right or not. Fix: calibrated confidence and a short path to correction. See [Your AI will be confidently wrong. Design for it.](/blog-posts/designing-for-ai-failure-states)
  • Drift: each agent response invents a new layout or tone. Fix: an [agent-ready design system](/solutions/design-system-agent-readiness-audit) the agent builds from.

Agentic product design, AI UX and agent UX compared

These terms get used interchangeably. They describe different jobs.

**AI UX design** is designing an AI feature a person operates: a suggestion, a summary, a chat panel. **Agentic product design** is designing a product where the software acts, and the person supervises. **Agent UX**, sometimes called agent experience, is designing your product so that other companies' agents, such as a buyer's shopping assistant, can read and operate it. Aero works across all three, and most B2B products now need at least two. For the third, see [Agentic commerce is coming for your checkout flow](/blog-posts/designing-for-agentic-commerce).

How an engagement works

Work happens inside your product, your repository and your team's cadence, not in a separate design file that someone has to translate later.

  • Map: every action the agent can take, its consequence if wrong, and the person accountable for it
  • Design the gates: autonomy tiers, approval and review flows, reasoning display and recovery paths
  • Prototype in your codebase against your design system, so what you test is what ships
  • Instrument and hand over: adoption, verification time and override rate wired into your analytics, with the rules documented for your team

Proof and practice

Aero runs its own operations on an agentic system in daily production, with named human approval gates on anything that ships, so the patterns on this page are ones we live with, not ones we only recommend.

Lindsey, Aero's founder, leads every engagement and holds the design judgment at each gate.

  • AI-accelerated UX and UI design

    Pixels are commoditized. Brand-fluent, accessible systems aren't. Your team can ship the next screen. Keeping your fiftieth screen on-brand, on-pattern, and on-business-logic is a different problem - design system governance, and it's the discipline most internal teams haven't built yet. Aero pairs 15+ years of system practice with AI for what AI does well: parallel exploration, deep research, system-wide consistency. You get AI speed plus the discipline AI can't generate on its own.

  • Agent-ready design systems

    Agents now build from your design system, and they bring none of your team's context. We engineer and audit systems with machine-readable metadata and explicit anti-patterns, using Figma MCP, Cursor, and Claude, so agents stay on-brand and on-system while your team ships faster.

  • AI product strategy

    The fastest teams decide with evidence, not opinions. We instrument products with tools like Pendo and run AI-assisted experimentation, turning real user behavior into a continuous loop of what to build, test, and sharpen next.

Related projects

Further reading

FAQ

Common questions

What is agentic product design?

Agentic product design is the practice of designing software in which AI agents take actions on a person's behalf. The design work shifts from helping someone do a task to helping them delegate it, check the result and correct it quickly. It covers what the agent is allowed to do, when a person must approve, how reasoning is shown, and how the product recovers when the agent is wrong.

How is agentic product design different from AI UX design?

AI UX design usually means designing an AI feature a person operates, such as a chat panel or a suggestion. Agentic product design starts when the software acts on its own: sending, booking, filing or changing something. That adds approval rules scoped by risk, visible action history, undo and handoff, and keeping agent output consistent with the rest of the product.

Should every AI agent action require human approval?

No. When an agent asks for approval on everything, people approve without reading and the checkpoint stops protecting anyone. Aero sorts actions by consequence: routine, reversible actions run and are logged; undoable actions notify the person; irreversible or high-stakes actions, such as payments, deletions or anything sent to a customer, wait for explicit approval with the reasoning shown.

How do you measure whether an agentic feature is working?

Measure how people use and check the agent, not only whether the model is accurate. Useful signals are the share of eligible users who delegate a task, how long it takes to verify a result, how often people override or correct the agent, and whether they come back to it. Aero instruments these before launch so every design change can be tied to a movement in one of them.

Bring one agent feature to a 15-minute call

We will show you where its approval, recovery and consistency risks sit, and which one to fix first.

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