ScenarioAutonomous scientific discovery governance

AI Research Agent Director

Also called Principal Investigator (AI-augmented)

An AI Research Agent Director would direct AI agents running research programmes and validate what they find. Whether the role exists depends on how AI develops. If it does, it would decide which results a company can trust.

What they do

The director would set research questions and boundaries for teams of AI agents, then review how those agents plan, run and report their work. They would decide which experiments may go ahead and which need human sign-off. They would check findings against independent validation, look for results that cannot be reproduced, and stop programmes that drift. They would work with lab safety, legal and scientific leads, and write the standards for what counts as a verified result. They would also report plainly to leadership on what is established and what is only claimed.

What a strong hire delivers

In the first months, a strong hire would deliver a clear authorisation framework for agent-run research, so everyone knows who approves what. They would set up independent verification for key findings and agree stop conditions for experiments. They would also produce a first honest account of which agent outputs the company can rely on, and which it cannot yet.

How you can tell they are good

Ask them to walk through a result they once refused to accept and why. Good candidates describe specific checks, not general caution. Ask how they would test an agent's claim of a novel finding. Listen for independent replication, attention to instrument and biosafety limits, and comfort saying 'not yet verified'. Be wary of anyone who treats fluent reports as evidence.

Why it is hard to fill

Scarcity would be extreme because nobody would have done this job. It needs senior scientific judgement, assurance discipline and an understanding of agent behaviour, and those rarely sit in one person. Titles would vary, and the best candidates may not look like obvious matches. Most senior scientists have not governed automated work, and most governance people lack domain depth.

Where to find them

Today they would come from senior research leadership in pharma, labs and universities, especially principal investigators who run large programmes. Others would come from research assurance, quality and reproducibility roles, laboratory safety and biosafety, and lab automation teams. AI governance and assurance specialists from frontier labs and model providers would add the agent side.

Interview questions for an AI Research Agent Director

  1. Tell us about a research result you stopped or rejected after review. What did you check, and who disagreed?

    Strong answers give concrete checks, such as replication, controls or raw data review, and show they held their position under pressure. Worry if they describe only general rigour or have never overruled a senior colleague.

  2. An agent reports a novel finding across two disciplines. How would you decide whether to believe it?

    Look for independent replication, review of the agent's method and data, and expert input from both fields. They should separate plausibility from evidence. Worry if they accept a well-written report or rely on the agent to check itself.

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

Job description you can post

We are building research programmes where AI agents plan and run scientific work, and we need a senior person to direct them and decide what we can trust. You would set the standards for verification, authorisation and safe experiment execution, and report honestly on what our research has established.

What you will do

  • Set research questions and boundaries for AI agent programmes
  • Review how agents plan, run and report experiments
  • Define which experiments need human authorisation
  • Oversee independent validation and reproducibility checks
  • Work with lab safety and biosafety leads on instrument connections
  • Stop or redirect programmes that drift or fail checks
  • Report to leadership on verified and unverified findings

What you will bring

  • Led a research programme and made decisions on whether results were sound
  • Demonstrated work in research assurance, verification or reproducibility
  • Deep expertise in at least one scientific domain
  • Experience working with laboratory safety or biosafety processes
  • Clear understanding of how AI agents fail and how to test them
  • Ability to explain uncertainty plainly to non-specialists

Nice to have

  • Experience with lab automation or instrument control
  • Background in AI governance or assurance
  • Work across more than one scientific discipline

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

Depends on a scenario

End-to-end automated scientific discovery

What AI would need to do
Systems autonomously formulate, execute and verify novel scientific work across disciplines.
What organisations would need to decide
Labs can safely connect models to instruments with independent validation and authorisation.
What would rule it out
Scientific novelty, reproducibility or safe experiment execution remains unreliable.

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

Skills that matter

  • Scientific domain expertiseWorkingApply disciplinary theory and experimental standards
  • Lab automation and biosafetyWorkingIntegrate instruments and safe experiment execution
  • AI governance and assuranceWorkingAssign accountability and verify lifecycle controls
  • 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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