AI

Designing Agent Interfaces for High-Stakes Decisions

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AI systems are increasingly moving beyond summarisation and copilots into something more consequential: decision support. Across regulated industries, and some national infrastructure, organisations are beginning to deploy systems that recommend actions, draft decisions, prioritise cases, assess eligibility, identify risks, or guide frontline staff through operational processes. These systems promise substantial efficiency gains — often the … continue reading

Agentic Interfaces: Beyond the Chat Window

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For the last few years, AI product design has largely revolved around one thing: the chat interface. Prompt in, response out. But the next wave of AI products is shifting away from “chatbots” toward agentic interfaces — systems that can reason, take action, collaborate with tools, maintain context, and work toward goals over time. The … continue reading

AI and ML Design Resources for UX Designers

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I’ve come across a few really good resources for UX Designers getting into or involved in designing for complex apps and AI-enabled software. Let me know of other great links and I will add them to the list. Number one on my list is the Google People + AI Guidebook (aka PAIR). This is a huge and … continue reading

A Practical Introduction to AI Agents

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How they work, why they matter, and what you should know before building one AI agents are quickly becoming one of the most talked-about developments in applied AI. But despite the buzz, many people are still unsure what an “agent” actually is or how it differs from a standard large language model (LLM) prompt. This … continue reading

Adaptive Learning for AI Agents

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As AI agents become more autonomous and take on increasingly complex tasks, adaptive learning becomes a core requirement. Adaptive learning is the ability to update behaviour based on new information. While model fine-tuning used to be the dominant mechanism for adaptation, modern agent architectures allow for more flexible and lightweight options that work in real … continue reading

Designing Agents

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I’ve recently been working on an agentic fraud alert review system for a bank, where our agent monitors transaction alerts and reviews the transaction against context of the bank account as well as any open source intelligence it can gather. The agent system started as a chat interface – where users could ask questions and … continue reading

Good Models Die in Notebooks

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In large consulting projects and enterprise AI programs, too many AI and data science projects follow the same unhelpful pattern. A team of data scientists works for months in notebooks, training and tuning models, carefully optimising precision, recall, and accuracy. Eventually, the results are presented: metrics are up, charts point in the right direction, and … continue reading

Thumbs Up to the Humans in the Loop

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Back in the early days of building apps, when we were still deploying things with FTP and debugging with alert(), one of the most thrilling moments was just watching someone use your thing. Not a simulated user, not a test suite, but a real human being poking around, getting confused, lighting up when something worked, … continue reading

Thinking beyond chat interfaces for human-agent interaction

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From Chat to Click Chat-based interfaces are the go-to solution for agent interactions, helping new users of agent systems navigate complex workflows. Chat is a flexible interface, and can return different formats of responses. There’s also no limit to what the user can input, if they can think of the prompt. However the user has … continue reading

How do you design explainability in Complex Systems?

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AI has become a driving force in software from personalised recommendations to critical decisions in healthcare and finance. This creates a challenge for users: how do we ensure that AI systems remain understandable and trustworthy to the people who use them? There are a few ways you can help build trust in AI applications, or … continue reading