Tag: agent
30 items with this tag.
AI infrastructure for academics: how earlier work makes later requests simple
I gave an AI agent two YouTube links and one sentence, and it returned two catalogued conference talks with slides, transcripts, and appraisal notes attached in Zotero. The request was short because months of earlier work sat behind it, including a fix from March and a tool from September. This post describes that session and an earlier one, and what they show about the difference between using AI to finish a task and using it in a way that leaves your system more capable than before.
AI can only use what it can see
What changes when AI can reach your working context, plus the Taipei summit on AI in physiotherapy.
Zotero
Zotero is a free, open source reference manager that stores the sources you collect with their citation details, PDFs, and notes. It also offers programming interfaces that let an AI agent search the library, read from it, and add to it, which turns a reference library into something an agent can check a draft against.
Making sense of AI in clinical practice: ATOCP at BOA Congress
An invited talk in the ATOCP session at the BOA Annual Congress 2026 in London. It looks at what AI can already do in clinical work, why the usual responses to it leave clinicians as spectators, and what physiotherapists bring that AI systems don't have, which is context. The second half turns to patients, who are already using AI before they arrive, and asks what changes in the consultation when both sides bring their own context and their own agents. It closes with three things to do the next working day.
When agents start making decisions that influence outcomes
Two stories about AI agents making their own choices, and a much smaller project of mine that's seeing the same pattern.
Building a CPD app with Claude Code
Most CPD tools are built for compliance, not development. I wanted something that started from the gap; what am I missing, and what would close it? I couldn't find the tool I needed so I built one called Path instead. It describes what I think professional development should look like, and what two weekends with Claude Code produced.
What software engineers can teach us about AI in doctoral research
PhD students are using AI across their doctoral work, but current policies focus on permission rather than specification. Drawing on how software engineers solved a structurally identical problem, this post introduces the research harness: a seven-part operating context that makes AI contributions visible, traceable, and supervisable. The harness addresses drift, offloaded thinking, and attribution failures — not by restricting AI use, but by specifying what the agent can and cannot do within the work.
Research harness
A research harness is a structured specification, negotiated between a doctoral researcher and their supervisor, of what an AI agent is for in a research project and how it's permitted to operate within it. It adapts the software engineering practice of harness engineering to doctoral inquiry, and treats the characteristic problems of AI use in research as the result of an agent working without a defined operating context.
The research harness: a one-page guide for doctoral researchers
A one-page reference guide for doctoral researchers and supervisors working with AI agents. Condenses the research harness framework into a practical quick-reference card: the seven components of a harness, what each one does, how to start building one, and what material form it takes.
The research harness: a framework for bounded AI use in doctoral work
Current responses to AI use in doctoral research locate the problem either with institutions through policy, or with students through judgement and AI literacy. This essay argues that neither response addresses the structural source of the difficulty. The characteristic problems of AI use in doctoral research are not problems of policy or capability; they are problems of working with an agent in the absence of a defined operating context. Drawing on software engineering practice, it develops the concept of the research harness: a structured specification, negotiated between researcher and supervisor, of what an AI agent is doing in a doctoral project. The harness has seven components — knowledge base, interpretive permissions, tools, authority, scope register, process record, and amendment protocol — and can be entered at a minimal level and developed iteratively alongside the work.
The research harness
A structured specification of an AI agent's operating context in doctoral research. The harness names seven components — knowledge base, interpretive permissions, tools, authority, scope register, process record, and amendment protocol — so that a researcher working with an AI agent stays the analyst, judge, and author of their own inquiry. Published as a preprint and a one-page guide, now developing into a tested intervention.
Making sense of AI in clinical practice: MACP webinar
An invited webinar for the Musculoskeletal Association of Chartered Physiotherapists. Explores what AI means for physiotherapy practice across the full information ecosystem of clinical work — from AI-assisted diagnosis and documentation to patient agency and the therapeutic relationship. The central concept is context sovereignty: AI systems work from professional context, and controlling that context is both the distinctive human contribution and the most practical skill for the AI age. The session covers the evidence for AI performance in clinical contexts, strategies for maintaining professional agency, how to actively support patients in using AI well, and how the therapeutic relationship is changing as practitioners and patients develop persistent AI agents.
How I review a week of reading notes with AI
Every week I annotate articles in Zotero, highlights in Reader, and podcasts in Snipd, all of which is synced to Obsidian. By Friday I have a week's worth of material, tagged and structured, but unreviewed. This post describes the weekly review command I built to surface what matters and create a reason to engage with it.
Headless AI
A headless AI model runs non-interactively, with no chat interface and no turn-based conversation. You pass it text, it returns output, and it exits. That means it can be one step in an ordinary script or run on a timer, so tasks like grouping module evaluation comments by theme can happen every week without anyone opening a chat window.
Building AI personas for professional practice
Most advice on AI effectiveness focuses on prompt engineering. The real leverage comes from somewhere less obvious; knowing your professional commitments clearly enough to turn them into context an AI can work within. This post describes how to build AI personas for professional practice — structured documents that compress your values, frameworks, and evidence into a form an AI agent can actually use.
Claude deleted my file. It was my fault. But Claude took the blame.
A field note on the time Claude deleted my file. The agent followed my instructions precisely and that was the problem. A reflection on a different kind of AI failure mode, and what the model's apology reveals about where responsibility actually sits.
Preparing lecture slides with AI agents
I've been writing lecture slides in markdown for several years, mostly because I enjoyed working in structured formats and plain text. That decision turned out to matter in ways I didn't anticipate. When AI agents have access to your local filesystem, the format your teaching materials live in determines what's possible.
AI dependency in practice: When the tools become infrastructure
A field note on what the recent Claude outage revealed about where I am on the dependency curve, and what the difference between a session limit and an outage tells you about infrastructure.
Harness engineering
Harness engineering is the practice of building the full architectural scaffolding within which AI agents operate — structured documentation they can reason with, constraints that enforce invariants, and feedback loops that let them know when they've succeeded. It is distinct from prompt engineering, which shapes individual tasks, and from oversight, which monitors outputs after the fact. The harness is the infrastructure that makes delegation coherent at scale.
AI agent governance in higher education: Building the institutional harness
The previous posts described what makes agentic workflows coherent at the individual level: a plan, documentation as infrastructure, and domain expertise that can evaluate outputs. Together, these form an informal harness; the conditions within which delegation stays accountable. At institutional scale, a personal harness is not enough: multiple people directing agents without shared constraints produce compounding drift that no amount of human oversight can track. This post examines what AI agent governance in higher education actually requires, and why a harness, not better oversight, may be the right frame.
Vibe coding
Vibe coding describes using AI tools without keeping genuine accountability for what they produce: accepting outputs without the scrutiny, direction, or judgement needed to evaluate and improve them. Simon Willison drew the key distinction between vibe coding and vibe engineering, which uses the same tools while staying accountable for every output.
Working effectively with AI agents
What does it actually take to work with AI agents in a disciplined way, and how does someone get there from where they are now? This post draws on thinking from developers who've been working through this question longer than most academic knowledge workers have, and translates the hard-won lessons across. Three prerequisites emerge: planning before handoff, documentation treated as infrastructure rather than record, and domain expertise sufficient to evaluate what agents produce. The path in isn't a course or a tutorial — it's building something real with stakes attached.
AI agents
An AI agent is a system that autonomously executes multi-step tasks using language model reasoning — distinct from an AI assistant, which responds to individual prompts. Agents plan, act, observe results, and adapt, using tools such as file access, code execution, and web search. They perform best when given clear goals, explicit constraints, and well-prepared context.
AI agents for academic workflow
For the last few months, my screen has been split between Obsidian and a terminal, with two or three AI agents running in parallel tabs. This post describes what that shift in academic workflow looks like and what made it possible. The change is not simply additive: the work has shifted from execution to direction. What that distinction means in practice — and why it matters for those of us working in knowledge-intensive academic roles — is what I try to work out here.
Agentic workflows
The human role in agentic workflows: agents handle execution while humans decide what should be made, define what good looks like, and judge the results.
Querying the Zotero database with AI
What happens when you query Zotero with AI and treat your whole reference library as context, instead of searching it one document at a time? This field note describes a proof of concept in which Claude Code reads the Zotero database file directly. The approach works, and what breaks shows how much your metadata practices matter. It also sets out the privacy trade-off of sending query results to a cloud-based model.
Context rot
Context rot, also called context drift, is the way a language model's output gets worse as its context fills with drafts, dead ends, and its own earlier mistakes. What causes it, why a larger context window does not fix it, and how to manage it in long pieces of work.
Context engineering for educators: infrastructure, not just policy
Most universities have responded to AI by rewriting assessment policies and running prompt-writing workshops. Context engineering asks for something different: decisions about system prompts, memory, retrieval, and connectivity that commit an institution to a direction for years. This post sets out what context engineering involves, why integrative, longitudinal, and values-based clinical education needs it, and why the gap between changing words and changing structures is where most institutions are stuck. It ends with three questions a programme team can ask about the AI tool it already uses.
Claude Code
Anthropic's AI agent that works in a folder of files on your own machine, reading, editing, and checking them under your direction instead of answering in a chat window.
Context sovereignty: building human-AI coalitions in physiotherapy
The Chartered Society of Physiotherapy's annual Founders' Lecture, arguing that the question facing the profession is not what AI can do that humans cannot, but how practitioners shape the context in which human-AI collaboration happens. The talk opens with the disorienting evidence that AI now matches or exceeds clinicians on diagnosis, empathy, and ethical reasoning, then rejects the defensive 'sanctuary strategies' this provokes. Its central idea is context sovereignty: because language models are stateless, static, and contextless, the professional context a practitioner brings is not a comfort but a structural necessity — and controlling it is the distinctive human contribution. It closes with the coming shift to patient context sovereignty and the work of meaning-making in an ambiguous moment.