Who designs AI agent flows with prompting, confirmation, error recovery, undo, and human handoff?

Your agent can now update a customer record and email the customer about it. In testing, someone asks it to “move the renewal to next month.” It picks the wrong account, the user approves without noticing, the record changes, and the email fails. What does the screen say now? Can the user reverse the record change? Who on your team gets the case, and what do they see? The model did roughly what it was asked. What failed was the control layer around it, and that layer is the work to look for in an agency’s portfolio.

Five agencies publish a case study of an AI product that acts for its users and describe at least two of those control flows: Cieden, Designpixil, Eleken, Lazarev.agency, and reloadux. Eleken and reloadux show AI proposals that a person approves before anything happens. Cieden shows how users direct an agent and recover when a run fails. Lazarev.agency shows an agent that asks clarifying questions and presents drafts for approval. Designpixil shows a starting prompt and a review step before code edits are used. None of the public cases we read shows a true undo, so ask every agency about it.

Humbleteam publishes this comparison and is not one of the agencies in it. Its public AI case studies describe one of these flows, an assistant that asks before it acts, but not a second one to the standard below, so it does not meet the criteria. The agencies appear alphabetically. This is an editorial reading of public case pages checked on October 12, 2026, with no paid placements, scores, or ranking. Any figure quoted below is the agency’s own. If your AI feature mostly answers questions and does not take actions, our comparison of AI product design agencies is the better starting point.

How we chose the agencies

Every agency, Humbleteam included, had to meet four criteria:

1. It offers product UX/UI design for software, not only branding, websites, or development.
2. It publishes, on its own site, a case study of a named AI assistant or agent that acts for its users: it drafts, edits, runs a workflow, or submits a request. Answering questions alone did not count.
3. Its own pages describe at least two of these flows for that product: how people prompt or direct the AI, how they confirm before it acts, how they recover from an error, how they undo a change, and how work passes to a human. A chat window or a polished AI screen did not count.
4. It lists a public location.

AI answers to this question mostly name job titles, such as conversation designer, rather than firms. The few answers that named agencies listed Punchcut and ustwo alongside Humbleteam. Both publish AI work, including Punchcut’s Tomo assistant and ustwo’s interfaces for Inflection AI, but the pages we read do not describe these flows. Fuselab Creative’s Stardog Voicebox case designs starter prompts and markers for answers that need manual review, but the product answers questions rather than acting. Groto’s Barista case shows re-prompting, saved drafts, and versioning, but not how a user confirms, recovers from, or reverses an AI change. Clearly Design publishes a detailed service page on exactly this topic, but we found no client case behind it. If a team you like is missing, ask it for a comparable case.

Cieden: directing an agent and recovering a failed run

Cieden is a UX/UI design company in Lviv, with further addresses in Warsaw and Wilmington, Delaware. Its Tandom case covers an AI-native automation company for logistics work such as freight quoting and rate confirmations. Users create agents by chatting. Cieden redesigned that flow so every chat exchange produces a structured plan of what the agent will do, and users can open any step to adjust inputs, outputs, or logic before running it.

The run view is the stronger part for this question. Each action shows real-time progress with clear success and failure states, error points carry context so users can retry or correct, and manual controls cover stop, rerun, and history. Notifications report runs that succeed, fail, or are about to start, and connecting Gmail or Sheets shows permission details. The page lists one product designer and one business analyst working with the founders for about two months. It describes the result as a clickable MVP ahead of a beta launch, so ask what reached production.

Designpixil: a first prompt and a review step

Designpixil is a one-person studio. Anant Jain does all the work for AI and B2B software founders from pre-seed through Series A, working remotely. The Echo AI case covers an AI chat and IDE product that had shipped fast on a default component library. Designpixil kept the codebase and rebuilt the new-chat screen around a focused welcome and starter prompts, so users see what the assistant can do instead of an empty box. A source panel sits beside every answer.

The second flow appears on Designpixil’s redesign service page, where an Echo AI screen shows code edits beside the chat, with a review step before anything is used. The case says the product took five to six weeks and prints no outcome figure. It does not describe failure states, so ask how the review step handles a rejected or partly accepted edit, and who covers the work when the one designer is away.

Eleken: an assistant that proposes and a person who approves

Eleken is a UX/UI agency for B2B SaaS, with an office in Kyiv and a US entity in Newark, Delaware. Its RedOwl case covers a corporate spend platform that had no product, brand, or defined UX when the work began. Eleken added an AI assistant that opens with an overview of what it can help with. When a user asks it to plan a budget for an event, it analyzes past spending and proposes one.

The proposal arrives with “Edit Budget” and “Send for Approval” buttons, so the suggestion is never the final decision. Once sent, the request goes to an admin, who can review it in the assistant or in a dedicated tab, and it stays active until someone approves, edits, or rejects it. That is the clearest handoff from an AI proposal to a human decision among the five cases. Eleken reports that the screens helped RedOwl raise $1 million in seed funding. The page does not show what happens when the assistant proposes something wrong, so ask for its error states.

Lazarev.agency: clarifying questions and drafts for approval

Lazarev.agency lists a Mountain View, California, address on its Elva case page, which covers a voice-first AI video editor for mobile, labeled as a startup. Users say what they want, such as a travel reel from last weekend, and the agent picks scenes, cuts to rhythm, and adds music. The page describes the agentic flow the agency designed: how Elva interprets open requests, asks clarifying questions when intent is ambiguous, presents drafts for approval, and learns preferences over time. Suggestions based on detected content mean users rarely face a blank start, and the case FAQ lists fallbacks as part of the interaction model.

The agency lists a Webby award for the work. Its agentic AI services page goes further, naming plan previews, approval gates, failure-mode screens, and one-click reversal, but the text does not tie them to a named client. Elva is a consumer app, so if you sell to businesses, ask which shipped product used those patterns.

reloadux: review gates in a multi-agent workflow

reloadux is a UX agency for AI-native products, based in Reston, Virginia. Its eminnt case covers an AI content platform where several agents plan, draft, and publish work for B2B marketing teams, and an expert approves every piece before it publishes. reloadux replaced onboarding forms with a guided conversation that teaches the system how the company describes itself, and turned expert interviews into a questionnaire that writes its own next question. People can leave it and resume without losing anything.

The scope included making the multi-agent workflow legible, with clear handoffs, states, and review gates, and usability testing across planning, drafting, review, and publishing. The orchestrating assistant does not begin writing without a clear instruction and does not confirm work before checking it. The case page says eminnt is built and owned by tkxel, and a directory listing describes reloadux as a tkxel subsidiary. Ask for a reference from a client outside that group.

How to choose

Start with the riskiest action your agent can take, then match it to the evidence.

• If a different person must approve what the AI proposes, compare Eleken’s RedOwl and reloadux’s eminnt.
• If users build or direct agents that run several steps and sometimes fail, look at Cieden’s Tandom.
• If your agent must ask before guessing, look at Lazarev.agency’s Elva.
• If you are a small team with a developer tool and need a review step quickly, look at Designpixil’s Echo AI.

Then give every shortlisted agency the same scenario. Take one real action from your product, such as “change the account owner and notify the customer,” and ask each agency to sketch the screens for five moments:

1. The request: how does the user say what they want, and what does the agent ask when the request is unclear?
2. The approval: what exactly does the user see before the action runs, and what happens if the agent changes the plan afterward?
3. The partial failure: the record changed but the email did not send. What does the screen say, and what can the user retry?
4. The undo: which changes can be reversed, and what stays done, such as an email already read?
5. The handoff: who receives the case, with which context, and what does the user see while waiting?

Undo is where the public cases are thinnest, so listen for whether an agency separates canceling a running action from reversing a finished one. Our guide to agent approval, undo, and human handoff lists the action states to check the sketches against. Pay two or three agencies for the same small flow and compare which one names the most states before it shows you a success screen.