Platform intermediary interests
A provider-and-interest disclosure surface shown when a third-party agent acts on a citizen's behalf, naming the provider, principal and any commercial relationship, with a one-tap switch to the public-option agent.
When a citizen's agent is supplied by their bank, employer, or insurer, it may be optimizing for the provider's interests as much as the citizen's, and the citizen usually cannot see it. Pointed at a government service, that agent might file a tax return in a way that favors the provider's products, or downplay an employer's liability in a claim.
The challenge is to surface whose interests an agent actually serves when it acts for a citizen with government, and to give the citizen a route that is not compromised.
When a commercially provided agent acts on a citizen at a government service, government needs to know whose interests are actually steering it, so an undisclosed conflict cannot shape an outcome unseen. Meeting that requires the agent to declare its provider, principal and commercial relationships and to attest whose interests it serves as part of the delegation chain the agency can check.
Government services must accept agents from any provider (interoperability) but must also offer the public-option agent so citizens are never forced to use a commercially compromised agent. The conflict and its disclosure must be legible to a citizen who would otherwise never know their agent was not acting solely in their interest.
When a third-party agent acts for the citizen, the surface names the agent's principal and commercial ties and offers a switch to the public-option agent.
No surface has been built yet; the approach above is the brief for one.
Emerging. The agentic inequality framework provides the analytical foundation. The regulatory response is nascent. No government has yet implemented provider-disclosure requirements for AI agents interacting with public services.
Agentic inequality framework. Research from the University of Oxford and the Cooperative AI Foundation (2025) defines "agentic inequality" as "disparities in power, opportunity, and outcomes arising from unequal access to, and capabilities of, AI agents," analyzed across availability, quality and quantity. It distinguishes this from earlier digital divides: "agents function as autonomous delegates rather than tools, generating new asymmetries through scalable goal delegation and direct agent-to-agent competition."
Direct. In a government services context, the "whose model" problem manifests when a bank-provided agent filing a tax return optimizes for the bank's financial products rather than the citizen's tax position; an employer-provided agent applying for a workplace injury claim downplays the employer's liability; or an insurer-provided agent navigating a health claim steers toward lower-cost treatments. The citizen may never know their agent was not acting solely in their interest.
The pattern requires mandatory disclosure of the agent's provider, principal and commercial relationships; interoperable acceptance of any provider's agent alongside a public-option agent; and agent attestation of "whose interests it serves" as part of the delegation chain.
Disclosure addresses transparency but not the underlying misalignment, which is why the citizen needs an uncompromised alternative as well as a warning. The harder part is connecting a disclosed conflict to a citizen who can actually act on it, and that connection is where the pattern is least settled.
The failure mode is a structurally misaligned intermediary quietly steering a citizen's government interaction toward the provider's benefit. Requiring the agent to disclose whose interests it serves, and giving the citizen an uncompromised alternative, addresses the misalignment itself, which case-by-case scrutiny of each interaction cannot reach.