Solutions
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.
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.
Every agentic product we work on is designed across the same four layers. Each one maps to a failure we see in shipped products.
Four patterns account for most of the agentic features we are asked to rescue. Each has a design fix.
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. We work across all three, and most B2B products now need at least two. For the third, see Agentic commerce is coming for your checkout flow.
AI UX design covers an AI feature a person operates. We design the layer beyond it, where the software acts and the person supervises: approval rules ranked by consequence, a visible action history, and recovery paths. Where an agency hands over files for your team to integrate, we work inside your product, repository and cadence, so what you test is what ships.
Work happens inside your product, your repository and your team's cadence, not in a separate design file that someone has to translate later.
We run our 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.
Every engagement has a design lead who holds the design judgment at each gate.
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, the discipline most internal teams haven't built yet. We've been designing since 2014, and we pair that experience with AI for what AI does well: parallel exploration, deep research and system-wide consistency. You get AI speed plus the discipline AI can't generate on its own.
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.
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.
Underwrite.ai applies AI-first models influenced by genomics and particle physics to deliver rapid, nonlinear credit risk assessments that significantly improve predictive accuracy. It does so with full explainability, zero up-front costs, and compliance baked in.
Rehinged.ai ingests and scores diverse, large-scale healthcare data. It delivers real-time, AI-ranked provider matches that fuel smarter sales outreach and pinpoint buyers who truly need your solution.
AI trust and transparencyBy Lindsey J
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Agentic product designBy Lindsey J
6 min read
FAQ
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.
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.
No. When an agent asks for approval on everything, people approve without reading and the checkpoint stops protecting anyone. We sort 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.
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. We instrument these before launch so every design change can be tied to a movement in one of them.
We will show you where its approval, recovery and consistency risks sit, and which one to fix first.
