--- title: "Speaking" author: Dan Birman url: https://dbirman.com/speaking updated: 2026-11-09 summary: "I speak about designing AI that people can hand real decisions to." --- # Speaking I speak about designing AI that people can hand real decisions to. Each talk draws on products I’ve designed and on [Agent Trust Patterns](https://dbirman.com/patterns), and includes a working demo. Talks run 20, 35 or 45 minutes. For events and podcasts: [hi@dbirman.com](mailto:hi@dbirman.com) ## Trust is structural When an AI product wants to seem trustworthy, it usually reaches for the copy: a warmer tone, or one more “Are you sure?” I think trust comes from structure. People hand over a decision when they can see the limits, get a receipt for what happened, and undo it if it goes wrong. At Uber, tipping had to reward good drivers without making riders feel guilty. We tested asking for the tip first and dropped it. The fix was in the order of the screens: the version that shipped asks for a rating first, and the tip appears with five stars. Ruli launched as a chat with no visible sources and no sign of how confident an answer was. We set three rules for how the AI behaves: show what it’s doing and why, let people undo any AI action, and let anyone flag or correct an answer. Then I apply the same test to agents that act for people, using the patterns and my [review of ChatGPT Work](https://dbirman.com/review-chatgpt-work), an agent whose judgment is better than its interface. ### Takeaways 1. **Trust comes from limits, receipts and undo.** If a product is missing one of them, better copy won’t make up for it. 2. **Ask when people are ready to answer.** Uber’s tip waits until the rider has rated the trip. 3. **Design the wrong answer first.** How a product behaves when the AI is wrong decides whether people give it the next task. ## Interrupt on drift, not on danger Most AI agents decide when to ask by the kind of action: before they send, pay or delete. [Anthropic reported](https://www.anthropic.com/research/claude-code-auto-mode) that Claude Code users approve 93% of permission prompts. The steps that matter often look routine: a price taken from a review site instead of the venue, an email to a new recipient, a skipped step. A rule about kinds of action can’t tell a step you planned from one you didn’t. This talk proposes a different trigger. Once you approve a plan, the plan is the baseline. The agent stays quiet while it follows the plan, even when it sends or spends within what you agreed. When it leaves the plan, it stops and says how. At the end of the run, it puts its own summary next to what it actually did, because an agent’s report can describe the plan instead of the work. I’ll demo the [working prototype](https://dbirman.com/drift) and show where the pattern breaks: a bad plan makes a quiet agent, and some steps need a hard stop whatever the plan says. Longer versions cover all six patterns. ### Takeaways 1. **Make the approved plan the baseline.** Interrupt when the agent leaves it, whatever kind of action it is. 2. **Name what changed.** Each pause should say how the agent left the plan, so the person can judge that one step. 3. **Put the report next to the record.** Show what the agent said it did beside what it actually did. ## From controllers to agents I trained as an industrial designer at RISD, and I still think about products as things you hold. A good physical control earns trust before you read anything: you can see how it’s set, it responds the moment you press it, and you can put it back. Most software agents act out of sight and report in their own words, so you’re left to work out what changed. At Backbone I led design across the controller and the app, and the Backbone button and the screen it opens were designed as one thing. When we made clips easy to share, each round moved more of the work onto the controller, with button hints along the bottom of the screen. The talk carries those qualities over to agents, with demos from the patterns, then shows where the comparison breaks. A controller works the same way every day. An agent’s record changes, so its controls should loosen as it earns room on a task and tighten again when the record slips. ### Takeaways 1. **Show the setting.** What an agent is allowed to do right now should be as easy to read as a switch. 2. **Answer right away.** Feedback should arrive the moment someone acts, in the place they’re already looking. 3. **Let the controls follow the record.** Give an agent more room on a task it has done well, and take the room back when it slips. ## Bios ### Short (75 words) Dan Birman designs how people work with AI agents: what an agent may do, when it should ask, and how it earns more room. He is Head of Design at Caplight and founded his own studio, Fora. Over about 12 years he has designed tipping at Uber, virtual care at Vida Health, the Backbone One controller and AI legal research at Ruli. A RISD-trained industrial designer, he publishes Agent Trust Patterns and Agent Trust Reviews. ### Long (150 words) Dan Birman designs how people work with AI agents: what an agent may do, when it should ask, and how it earns more room. He is Head of Design at Caplight and founded his studio, Fora. Over about 12 years he has led design for rating and tipping at Uber, built the design team at Vida Health, a virtual care company, and led design for an AI legal assistant at Ruli. At Backbone, which turns a phone into a game console, he led design across the controller, the app and the brand. Trained in industrial design at RISD, he still thinks about software as something you hold. His Agent Trust Patterns are six interface patterns for AI agents that act on people’s behalf, each with a demo. Each Agent Trust Review tests a real agent product against them. He speaks about designing AI that people can hand real decisions to. [Download a photo (JPG)](https://dbirman.com/Dan_Birman_photo.jpg) ## What I need An HDMI or USB-C connection to the screen, a clicker, and internet access for the demos, which run in a browser.