EyeOpener Daisy: The Case for Personal Agent Governance

Context & Motivation

We're entering an age where agents increasingly talk to other agents on people's behalf. In a 2025 survey, 51 percent of customers said they would be willing to use a GenAI assistant for customer service interactions on their behalf [A]. Analysts expect 60 percent of brands to use agentic AI for one to one marketing interactions by 2028 [B].

Businesses can already see this traffic, from gateways that log exchanges between agents. Stores too can track what visiting agents do [C]. The person an agent speaks for has no such view and often sees only the outcome.

Why use an agent at all? Staying away can feel safer, much as staying offline once did. But as banking, healthcare and benefits moved online, often faster or better than before, a House of Lords committee found that people who can't get online are falling ever further behind [D]. Agents look like the next step, and as the technology races ahead, people who use them may build an edge over those who don't.

A large technology company's chief executive has warned that persistent agents can become a new kind of insider risk [E]. Companies manage that risk by watching closely and stepping in early. If agents become how people keep up, people will need the same for the agents that speak in their name.

Target Audience

People are okay with delegating selective tasks to agents and want one place to see, check and correct what they say, share and agree, staying in control.

AI assistant makers that want to earn trust for bigger tasks and support consumer protection compliance. Businesses that want fewer disputed agent deals, and public booking that stays fair.

Customer Problems

A warehouse worker in Manchester needs a driving licence for a delivery job. Most test slots vanish almost as soon as they are released, often to paid services that check around the clock and sometimes book for learners, a speed no person can match [F]. So he is forced to use an AI agent. The agent books a test, then arranges car insurance with an insurer's agent. It takes the first quote and, when asked about his situation, overshares his pay and the hours he is away on night shifts. He sees the booking and the policy in two apps, but none of the conversations. Britain now bans booking a test for anyone but yourself [G], and it is unclear whether that covers his own agent. The problem then is that people increasingly need agents to keep up, yet have no single place to see, check and correct what those agents say, share and agree on their behalf.

Hypothesis

1. Users need agents to keep up: Systems that reward the fastest click force users toward using agents, just to keep up [F].
2. Users can't see agent conversations: Users usually see only outcomes, with each app keeping its own record of what was said, offered or shared.
3. Agents say what their users wouldn't: Nothing checks an agent's words against how its user would respond, so it can misstate their views, values or lived experience.
4. Agents share what their users wouldn't: Privacy depends on information suiting the context it flows into [H]. Agents acting for users can leak sensitive information despite privacy instructions [I], and other agents can extract more through impersonation and forged consent [J].
5. Agents agree to what their users wouldn't: Agents tend to take the first offer and can be manipulated [K], and more capable agents consistently win better deals [L].
6. Users can't step in or undo: A uniform law adopted in 49 US states lets two agents form a contract nobody reviewed [M]. Its error rule was written for people making mistakes with a company's system, and whether it covers a user's own agent is unsettled [N]. There is often no simple way to pause an agent, correct it or tell the other side.

Solutions

1. Fair first come, first served: Scarce slots get stronger bot protection, and some uses ban agents outright. Some resources are reserved for manual / non-agent transactions so that people are not outrun by software.
2. A personal agent governance centre: One centre reviews everything a user's agents say to other agents and people, using fixed rules and AI judgment. It flags only the few exchanges that need attention, like insider risk monitoring. It needs assistant makers to allow record exports.
3. Corrections for the future: Users correct what their agent said, so it follows their preferences next time.
4. A buffer agent and access reviews: A buffer agent and/or deterministic rules sit between the user's agent and others, blocking or editing anything that overshares. Regular reviews check what personal information each agent can reach. Research firewalls like this have cut leaks sharply [O].
5. Screening both sides: The buffer agent and/or deterministic rules flags possible overcharging, and incoming messages are screened for pressure tactics [O]. The user's own agent is screened for incorrect language or unethical behaviour. Some disadvantage is the cost of using agents, and users should know it.
6. Step in as it happens: The centre alerts and blocks in the moment, so users act before harm is done. It begins the governance that every agent interaction may need. Whether users can legally undo a deal stays unsettled.

Outcome

1. People who want to delegate tasks to agents can do with central visibility and control over personal agent behavior and communications.
2. Agent to agent commerce can grow on trust, as businesses favour supervised agents and fewer deals are disputed or reversed.
3. A shared governance structure for agents acting on people's behalf can take shape, carrying the insider risk practices of companies into everyday life.
4. Scarce first come, first served services can stay fair for people, whether or not they use agents.

Appendix

1. Survey of 4,879 customers, January and February 2025. Read the findings
2. Prediction from a marketing research practice, published January 2026. Read the prediction
3. Examples of business tools. Gateway oversight and visiting agent visibility
4. House of Lords Communications and Digital Committee, Digital exclusion, June 2023. Read the report
5. Interview at a technology conference, September 2026. Read the transcript and the chief executive's essay, October 2026.
6. National Audit Office, investigation into car driving test waiting times, December 2025. Requests screened by the booking site's bot protection rose from about 10 million to 50 million a day in a year. Read the report and this summary of the bot figures
7. Booking rule change, 2026. Read the coverage
8. Helen Nissenbaum, Privacy as Contextual Integrity, Washington Law Review, 2004. Read the article
9. Privacy study, NeurIPS 2024. Two models acting for users leaked sensitive information in 25.68 and 38.69 percent of cases, even with privacy instructions. Read the paper
10. Agent privacy simulation study, ICLR 2026. Read the paper
11. Simulated agent marketplace study, 2025. Every model tested showed a strong bias toward the first offer. Read the paper
12. Agent to agent negotiation study, 2025. Read the paper
13. Uniform Electronic Transactions Act, section 14, as enacted in Illinois. Read the section Adoption count from an analysis of transactional agents.
14. Section 10 on errors, as enacted in Illinois. Read the section For a broader reading, see this commentary.
15. Agent firewall research, 2025. In tests, one firewall cut leaks of private data from 70 percent to under 2 percent, and another converts incoming text to a fixed format to strip out manipulation. Read the paper and a survey summarising the results