Agent Architecture and Protocol Strategy Artifacts
Human Review and Autonomy Control Simulator
SIMULATEDVerified Jul 2, 2026Autonomy is not one setting for every workflow. This artifact shows how throughput improves as human review decreases, and where edge case risk begins to exceed what the organization should accept.
Same instrument · three industries pick a use case to reconfigure the run
Prefer to read? The two minute case study · problem → approach → metric → outcome
Problem
Too much review can make automation uneconomic, and too little review can allow high risk edge cases to slip through. The right autonomy level depends on risk tier, impact, reversibility, and the organization's control posture.
Approach
The simulator processes a modeled queue across autonomy levels. It shows how throughput, human load, and edge case exposure change as review requirements loosen.
Why this way
This connects autonomy to operating capacity, risk exposure, customer impact, compliance, and workforce design.
The metric
Edge case coverage versus throughput; the recommended (highest zero slip) level.
The trade-off
More autonomy means more throughput and, eventually, an unreviewed high severity error.
Outcome
The autonomy level to run per risk tier, the most speed that still catches every edge.
Items processed
Items sent to a human
Unreviewed errors
Cost of the slips
The queue · ● edge case
L1 clears every edge case
Autonomy by risk tier · bridges EL-05 / Govern
The level isn't a global setting, it's set per use case by its risk tier.
If you act on this · the call → expected lift → how you'd measure it
The call
Set autonomy by risk tier and impact, not by automation enthusiasm.
Expected lift · illustrative
Increases throughput while keeping edge case exposure within an acceptable control boundary.
How you'd measure it
Review load, throughput, edge case slip through rate, escalation rate, control exceptions.
Steering committee takeaway: The question is not how much autonomy is possible. The question is how much autonomy is appropriate for the risk tier.
How this is built
Twenty items carry a risk tier and four are edge cases (errors if auto approved). Each level defines a review policy; an edge case slips when it isn't reviewed. Throughput rises with autonomy; exposure = Σ severity of slipped edges. The medium risk edge is engineered to slip exactly one level past the balance point.
Stack: Next.js (static) + shared design system; deterministic client side.
Limitations: this is a simplified queue simulation. Production workflows would require live performance data, risk policy, audit logging, escalation paths, and ongoing exception review.