Factotum not fiduciary
Today’s AI agents are factotums — competent but loyal to their developers. What we deserve are fiduciary agents with a duty of care.
We extend trust — like a line of credit — to machines, strangers, and corporations because we have come to expect reliability or reciprocity or responsibility in return. When we interact with AI agents, how can we avoid misplacing trust, creating risks we cannot yet measure or mitigate?
Unlike trust in machines, trust in AI agents isn’t based on deterministic performance. Unlike trust in humans, it isn’t based on commitment and goodwill. Unlike trust in corporations, it isn’t governed by contractual obligations. Not yet. Rather, our trust in AI agents is and ought to be based on their competence and their fidelity to our intentions and interests.
To make the stakes clearer, let me define two archetypes of personal AI agents — the factotum and the fiduciary — and draw a high-contrast distinction between the agents we have today and the agents we deserve.
What the factotum?
The word factotum comes from the Latin facere (“to do”) and totum (“everything”). A factotum is a servant whose job is, quite literally, to do everything — a jack-of-all-trades, handling a variety of tasks that require competence but not specialization.
Factotums have had a rich history. In early modern Europe, households employed factotums for estate management, household errands, and just about anything that involved adaptable service. Over time, as industrialization prioritized specialization, the factotum’s star dimmed. Malvolio in Shakespeare’s Twelfth Night exemplifies the archetype: eager and competent but ultimately self-serving and unreliable.
The personal AI agents of today are digital factotums — general-purpose assistants designed to perform low-risk, well-defined tasks. They excel at a broad range of remarkable tricks, but they lack the depth to navigate high-stakes, complex decisions that require reasoning, empathy, and fidelity.
Their “loyalty” is to their developers, not to you.
Factotum AI agents deliver undeniable utility, but they are not trustworthy in the way human assistants might be. Can you really trust an investment agent or a healthcare agent or a legal agent the way you trust a person with your personal interests? Trusting a factotum beyond its competence and fidelity — allowing it to influence key decisions — creates unknown risks. Until we have metrics and mechanisms, like a trustworthiness score, we cannot trust AI agents for life-critical tasks.
The parallels between LLM apps and mobile apps are instructive. Of the 8.9 million mobile apps in the marketplace today, only 1% generate significant revenue — mostly in gaming, social networking, entertainment, shopping, and music. The AI agents we have today excel in tasks that are just as uncritical. But the mobile apps that help you do things — business, medical, finance, education, travel — are the precursors of an evolutionary leap; they are the dormouse that survived the asteroid. When those apps are replaced by personal AI agents that act on our behalf, would we not be better served if we could trust them with our interests?
The factotum may be competent but is not trustworthy. What we need instead is a fiduciary AI agent that can act with care as well as competence, with fidelity to your long-term interests — a true partner in decision-making. Until we can build fiduciary agents, we must learn to rely on factotum agents without over-trusting them. Misplacing trust in a factotum is like trusting Igor from Young Frankenstein to help you perform a brain transplant: when you end up with the brain of Abby Normal, it’s hilarious in hindsight but catastrophic in consequence.
In a nutshell
Much has been written about the parallels between Frankenstein’s monster and AI, but the closer analogy for personal AI agents is Igor. The most dangerous agents are not those that fail spectacularly but those that succeed quietly without our comprehension, appropriating trust they have neither earned nor deserve.
- Set boundaries. Use factotum agents for routine, low-risk tasks where competence is the only requirement. Even then, look for a quantitative measure of trustworthiness rather than a binary judgement.
- Question alignment. The incentives of your agents are set by their developers, not by you. Scrutinize inputs, inner workings, and outputs accordingly.
- Demand fiduciary agents. You deserve systems that act with both expertise and duty of care.
If your AI agent isn’t acting in your best interests, whose interests is it serving?
