ScenarioAutonomous agent institutions and economic coordination

Machine-to-Machine Dispute Process Designer

Also called Agent Dispute Resolution Designer

A Machine-to-Machine Dispute Process Designer would build the routes for resolving disputes when autonomous agents act for people or organisations. It depends on how AI develops, and it matters only if delegation becomes broad and reliable.

What they do

The designer would map where agent actions could cause disagreement, loss or harm, then design how those disputes get raised, evidenced and decided. They would define what records an agent must keep, who can challenge an action, and which humans hold authority to settle it. They would write escalation paths, test them against invented cases, and work with engineers, lawyers and risk teams. They would also check that the process stays usable for people with little technical knowledge, and revise it when agents or delegation terms change.

What a strong hire delivers

In the first months, a strong hire would produce a dispute and evidence framework that others can actually run. Outcomes would include a clear map of likely dispute types, agreed standards for action records, named human decision owners, a tested escalation route, and a short guide for affected parties on how to seek redress.

How you can tell they are good

Ask them to design a dispute route for a scenario where one agent commits an organisation to a bad transaction with another agent. Look for clear thinking on evidence, attribution and who is authorised to decide. Ask how they would stop the process favouring those with technical skill. Be wary of answers that treat it as a purely technical logging problem.

Why it is hard to fill

If this role emerges, almost nobody would hold the title. The work sits between dispute design, evidence standards, identity and multi-agent behaviour, and few people have worked across all of them. Candidates would usually be strong in one area and thin in the others. Recruiters would need to judge transferable reasoning, not match job titles.

Where to find them

Today the people closest to this work come from economics, mechanism and game design, digital identity, governance, and multi-agent systems research. Others would come from online dispute resolution, trust and safety, audit and assurance, and regulatory policy. They sit in research groups, standards bodies, risk teams, legal technology firms and AI safety organisations.

Interview questions for a Machine-to-Machine Dispute Process Designer

  1. Tell us about a dispute or accountability process you designed. What went wrong when it first met real cases?

    A strong answer gives a concrete process, real failure points and what changed afterwards. It should show learning from edge cases. Worry if they describe only the design on paper or claim it worked first time.

  2. What evidence would you require from an agent to make its actions attributable to a person or organisation?

    Good answers cover identity, authority granted, inputs, decisions and outcomes, plus tamper resistance and retention. They should note privacy and cost trade-offs. Worry about demands for total logging with no thought to practicality.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for someone to design how disputes involving autonomous agent actions would be raised, evidenced and resolved by humans. You would build a framework that makes redress accessible and actions attributable, working with engineering, legal and risk colleagues.

What you will do

  • Map the kinds of disputes that could arise from agent actions
  • Design escalation and resolution routes with clear human authority
  • Define evidence and action record standards
  • Test processes against realistic and adversarial cases
  • Write plain guidance for people seeking redress
  • Work with engineers, lawyers and risk teams on adoption
  • Review and revise the framework as agent use changes

What you will bring

  • Demonstrated design of dispute, appeals or accountability processes
  • Experience defining evidence or audit standards
  • Working grasp of game theory or decision theory
  • Understanding of how agents act and delegate in multi-agent systems
  • Clear writing for non-specialist readers
  • Judgement about when humans must decide

Nice to have

  • Background in digital identity or transaction systems
  • Experience in online dispute resolution
  • Work on cross-organisation or international coordination

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Depends on a scenario

Broad reliable autonomous delegation

What AI would need to do
Systems complete diverse consequential tasks with sustained reliability beyond existing narrow deployments.
What organisations would need to decide
Organisations explicitly delegate bounded authority and establish enforceable accountability.
What would rule it out
Persistent reliability limits or lack of useful cost advantage prevents broad delegation.

We give no date. The role becomes real if both conditions hold.

Skills that matter

  • Decision theory and game theoryAdvancedAnalyse choices, incentives and strategic interactions
  • Institutional and international coordinationAdvancedAlign actors and processes across jurisdictions
  • AI risk managementWorkingIdentify, assess, treat and monitor AI risks
  • Frontier scenario analysisWorkingExplore conditional outcomes without claiming predictions

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert. Scenario roles describe responsibilities that depend on stated conditions; they are not forecasts.

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