In development

When your experts override AI, ask them why.

Whyloop captures the judgement experienced people never write down, at the moment it becomes visible: when they correct an AI agent.

Try the live demo Become a design partner

Human review is still the norm

Dynatrace, survey of 919 senior leaders, 2026
  • 69%

    of agentic AI decisions are still verified by a human.

  • 87%

    of organisations are building or deploying agents that need human supervision.

  • 13%

    of organisations use fully autonomous agents.

01  The problem

The knowledge that matters most is rarely written down.

Every one of those human reviews is a moment where an expert knows something the agent does not. Today, that knowledge disappears with the click.

  1. 01

    It lives in people

    Judgement, rules of thumb and exceptions built over years. Often hard to put into words, even for the expert.2

  2. 02

    It leaves with them

    People retire and change jobs. In some fields the most experienced part of the workforce is also the oldest.

  3. 03

    AI agents don’t have it

    Agents follow documented process and general patterns. Most enterprise AI tools don’t learn from or adapt to the workflows they are used in.3

“We can know more than we can tell.”

Michael Polanyi, The Tacit Dimension, 1966 2

02  Live demo

You’re the expert. Overrule the agent.

Pick a case, decide whether the agent got it right, and see what Whyloop keeps when you don’t. The cases are fictional.

  1. Proposal
  2. Decision
  3. Why
  4. Lesson
  5. Next case

03  Approach

Treat every override as a possible lesson.

Whyloop is being designed to work alongside AI agents, in any field where expert judgement makes the difference. We’re building it now, together with the companies that will use it.

  1. The expert overrides

    An agent proposes something. Someone who knows better rejects it or changes it.

  2. We ask why

    One short question, at the right moment. Not every time: knowing when not to ask matters as much.

  3. The answer is put to work

    Answers that hold up are kept and fed back, so the agent and the organisation both learn.

Why the reason matters more than the click

A reason carries more than a correction. In one hospital study, reading clinicians’ free-text reasons for overriding alerts exposed decision-support rules that were broken.4

Human judgement already shapes how today’s models are trained: through preferences between answers,5 demonstrations and rankings,6 and written critiques.7 Whyloop takes that idea to where expert feedback is scarcest: the everyday decisions people make after an agent is deployed.

04  Where it’s going

Three things one loop should give you.

  • A better agent

    Fewer repeats of the same mistake, because the agent learns from the people who corrected it.

  • Knowledge that stays

    Expertise remains in the organisation when the person who had it moves on.

  • Decisions you can see

    The implicit rules people actually work by, made readable for the first time.

05  Research

The work we build on.

Selected studies and reports behind the problem and the approach. Each opens the original source.

  1. 01 Industry survey
    January 2026     The Pulse of Agentic AI 2026 Dynatrace 919 senior leaders: 69% of agentic AI decisions are still verified by humans; 13% of organisations use fully autonomous agents.
  2. 02 Book
    1966     The Tacit Dimension Michael Polanyi, University of Chicago Press The classic account of tacit knowledge: skill and judgement that experts use but cannot fully articulate.
  3. 03 Report
    July 2025     The GenAI Divide: State of AI in Business 2025 MIT NANDA, as reported by Fortune Names a “learning gap”: enterprise AI tools stall because they don’t learn from or adapt to workflows.
  4. 04 Peer-reviewed
    JAMIA, 2019     Cranky comments: detecting clinical decision support malfunctions through free-text override reasons Aaron, McEvoy, Ray, Hickman and Wright Clinicians’ written reasons for overriding alerts revealed rules that were broken or could be improved.
  5. 05 Paper
    NeurIPS 2017     Deep reinforcement learning from human preferences Christiano, Leike, Brown, Martic, Legg and Amodei Shows that a small amount of human judgement can steer what a learning system optimises for.
  6. 06 Paper
    2022     Training language models to follow instructions with human feedback Ouyang et al. Human demonstrations and rankings, used as training signal, made models markedly more useful to people.
  7. 07 Paper
    2023     Training language models with language feedback at scale Scheurer et al. Written critiques, not only scores, can be turned into training signal that improves a model.

Whyloop is independent and not affiliated with any of the authors or organisations listed.

06  Design partners

We’re looking for design partners.

We’re looking for a small number of organisations to test Whyloop in their own work and help shape it, wherever expert judgement is valuable and hard to replace.

Who we’re looking for

  • You’re deploying, or planning to deploy, AI agents in expertise-heavy work.
  • You’re worried about what walks out the door when experienced people leave.
  • You’d like to help decide what this becomes, as a partner, advisor or collaborator.

What design partners get

  • A first look at the parts of the method we don’t publish.
  • Whyloop tested in your own work, with your own experts.
  • A direct say in what we build next.

Get in touch

Tell us a little about your organisation and the expert work you have in mind.

We only use your details to reply to you.