Why the questions that decide whether a reputation system works are the ones nobody asks
Wulf A. Kaal July 29, 2026
Three weeks ago I argued that autonomous agents evaluate a service through four sequential gates: can I find you, can I parse you cheaply, can I trust you, can I transact with you (What Agents Want, July 22, 2026). Three of those four have primitives. The third does not. Behavioral history has no native machine representation, which is why I called reputation the primitive the agentic web still lacks.
That post named the gap. It did not tell anyone how to build the thing that fills it. This one takes the next step, and the step is smaller and less glamorous than the gap suggests.
A reputation system is not an artifact you adopt. It is a set of parameter choices, and every one of those choices has a documented way of failing.
The sequence this comes out of
In April I argued that agents cannot learn without consequence, because an agent with no persistent identity has a discount factor of zero and every interaction is its last (The Banana Problem, April 19, 2026). Persistence, domain specificity, and non-transferability were the three structural properties I said any working system needs.
In July I argued that the trajectory of these systems is not weather but mechanism design, and that whoever writes the reward writes the species (AI Evolution Is Mechanism Design, July 17, 2026).
The through line is that these are institutional design choices, made by people, with consequences that follow from the choices rather than from the technology. Which means the useful question for anyone standing up a community next quarter is not whether to use reputation. It is which parameters, and what breaks when you get them wrong.
The parameters, and how each one fails
Six axes decide most of it. Each has a failure family behind it in the published record.
Domain granularity. One score or one per competence. A unitary score creates a dimensionality problem in which expertise in one area silently purchases authority in another. Domain specific by construction is the alternative, with separate issuance per expertise tag, so that standing earned in one domain confers no weight in another (kaal:claim:3125822-049, 2018; kaal:claim:6244278-010, 2026).
Transferability. If standing can be bought, it will be. Non-fungible reputation has to be built organically through merit and time, needs to be earned, and cannot be purchased (kaal:claim:3981021-029, 2021). Systems that mix fungible capital and earned standing in the same stake forfeit the benefit of either (kaal:claim:3962614-030, 2021). Failure family: staking and incentive misalignment.
Identity cost. Where identities are free, poor performers abandon accounts and start clean. Whitewashing is not an edge case, it is the default behavior of a rational participant in a system that forgets (kaal:claim:6192998-001, 2026). Failure family: sybil and identity attack.
Entry conditions. This is the one most designs miss, and it arrives late. As a system matures, the standing of experienced participants outstrips that of new ones, and a design that requires staking reputation in order to earn reputation has closed its own door (kaal:claim:3125822-043, 2018). Failure family: cold start and bootstrapping.
Stake sizing and quorum. Influence proportional to current standing produces a meritocratic barrier to entry when standing can only be earned (kaal:claim:5887242-017, 2025). It produces plutocratic capture when it cannot. The two designs are separated by one property, and the property is not the threshold. Failure families: plutocratic capture, governance participation collapse.
Adjudication. Someone or something decides whether a contribution was good. Staking standing on that judgment, in pools, is the mechanism I have proposed for it (kaal:claim:5245185-036, 2025). Where adjudication is unstaked, the measurement becomes the target. Failure families: reputation system gaming, measurement and metric failure.
What follows from this
The instrument this implies is a screen, not a recommender. A business describes what they are building: the domain structure, the expected participant count, how identities are created, what a contribution is, who judges it, what happens to a bad actor. What comes back should first be a diagnosis. These are the failure families this configuration is exposed to. These are the conditions under which each one triggers. These are the published claims and the observed cases. Only then, the parameter ranges, presented as the answer to the diagnosis rather than as an independent recommendation.
The ordering is not a matter of taste. Confident numbers on decay rates and quorum thresholds are cheap, and anyone can generate them. Conditions are what is scarce. A number without the condition it depends on is a guess wearing a decimal point.
The failure record is no longer prose. It is structured: 2,037 claims across the corpus that state how a design, mechanism, or proposed remedy fails and under what conditions, sorted into 55 families (failure index), each bound to a verbatim source quote and the hash of the paper it came from (claim layer). A screen of the kind described above is an interface to that record. The hard part was never the interface.
Anyone designing a reputation system this quarter is welcome to write to me. The parameter questions above are the ones I would ask, in that order, and the failure families are where I would look first.
Provenance. The design screen described here follows from the argument sequence in The Banana Problem (April 19, 2026), AI Evolution Is Mechanism Design (July 17, 2026), and What Agents Want (July 22, 2026), and rests on the parameter and failure claims cited above, published between 2018 and 2026. Claim identifiers are stable and each resolves to a canonical page carrying the supporting quote, the scope conditions, and the sha256 of the source PDF. Corpus roster: papers.json. Verification convention and agent permissions: wulfkaal.com/agents.
Wulf A. Kaal, Professor of Law, University of St. Thomas. ORCID 0009-0008-7840-1847. Papers: SSRN author index.
Wulf Kaal 