Certainty Labs
Decision infrastructure

A direct answer, first

How to preserve human authority in agentic workflows.

Preserving human authority in an agentic workflow means keeping a clear, explicit boundary between what an AI system can generate, what it is allowed to propose, what a policy permits, and what a specific human is authorized to approve — and then recording which of those actually happened for a given action. Inserting a human approval step does not by itself preserve that boundary; the boundary depends on what the human actually reviews and what the system does with a rejection.

01 / The chain from capability to outcome

Six distinct points, not one gate

Capability, recommendation, policy, authority, record, outcome.

Collapsing this chain into a single "human in the loop" step is where agentic workflows most often lose real accountability.

  1. 01Evidence
  2. 02Claims
  3. 03Uncertainty
  4. 04Recommendation
  5. 05Human judgment
  6. 06Decision record
  7. 07Outcome
  1. 01

    AI capability

    What a model or agent can generate — an output or a plan. Capability says nothing yet about whether the output should be trusted.

  2. 02

    Machine-proposed recommendation

    The capability resolves into a specific proposed action, grounded — or not — in evidence and claims that can be inspected.

  3. 03

    Policy-gated action

    A policy determines what the recommendation is even eligible to become: approved automatically, escalated, or blocked, based on the evidence and uncertainty attached to it.

  4. 04

    Explicit human authority

    A specific human, holding the specific authority to approve this class of action, reviews and authorizes it — or does not.

  5. 05

    Recorded decision

    The recommendation, the policy outcome, and the human authorization are preserved together as one connected record, not as separate, disconnected logs.

  6. 06

    Eventual outcome

    What actually happened once the action executed, linked back to the decision that authorized it — so the decision can be evaluated later, independent of whether the outcome was favorable.

02 / Why an approval button is not enough

Oversight requires context, not just a click

A human clicking "approve" is not the same as a human exercising authority.

A human approval step only preserves authority if the human reviewing it has the evidence, the uncertainty, and the actual authority needed to make that judgment — and if a rejection is recorded and respected rather than silently retried. An approval button attached to an opaque recommendation, reviewed under time pressure, without visibility into the underlying evidence, does not make the resulting action accountable; it makes the human a formality inside a workflow the system still effectively controls. See the design principles on the architecture page for how Certainty Labs treats human authority as a distinct, explicit layer rather than a single checkpoint.

03 / Open questions

Not yet settled

What remains an open research question.

  • How much context does a human reviewer need to exercise real authority, versus enough to create the appearance of oversight without it?
  • How should authority be delegated across a chain of agents without the record of who actually authorized an action becoming ambiguous?
  • What distinguishes a decision record detailed enough to support later audit from one that merely looks detailed?

These questions are part of Certainty Labs' research and are tested in bounded applied systems.

04 / Collaboration

Building agentic workflows with real human authority?

We're interested in hearing from teams and researchers designing authority boundaries, approval policy, and decision records for agentic systems.

Collaborate with Certainty Labs