Use Cases
Rather than describing this abstractly, here are concrete pictures of who ends up using AERA and what their experience actually looks like.
The hands-off holder
Who they are: someone who believes in owning tokenized stocks long-term but doesn't want the ongoing job of watching allocation drift or manually rebalancing.
What using AERA looks like for them: they deposit funds once, set a target allocation and risk tolerance (say, "I'm comfortable with moderate volatility, no more than 40% in any single sector"), and then largely leave it alone. Every so often, they get a notification: a rebalance happened, with a plain-English explanation of why. They can check their dashboard whenever curiosity strikes, but they don't need to.
The yield-conscious investor
Who they are: someone who doesn't want capital sitting idle, and wants a sensible, ongoing balance between equity exposure and on-chain yield, without manually shifting funds back and forth themselves.
What using AERA looks like for them: they set rules that let the agent shift the equity/yield balance as relative conditions change — for example, allowing more weight toward yield when rates rise meaningfully, and back toward equity when they fall. They're less focused on picking specific stocks and more focused on overall capital efficiency.
The risk-boundary setter
Who they are: someone with strong, specific opinions about what they will and won't hold — a hard cap on exposure to any one sector, or a firm rule against certain volatile assets — but who doesn't want to enforce that boundary manually, trade by trade, forever.
What using AERA looks like for them: they spend real time upfront setting precise rules (maximum single-position size, disallowed sectors, volatility ceilings), and then trust that those rules are mechanically enforced by the contract, not just "kept in mind" by a human or a model that might drift from them over time.
The transparency-first investor
Who they are: someone genuinely open to automated portfolio management, but only if every action can be independently understood and verified — not a black box they're expected to simply trust.
What using AERA looks like for them: they regularly review the decision log, cross-checking the stated reasoning against the actual market data at the time. For this kind of user, the explanation attached to every action isn't a nice bonus — it's the entire reason they're comfortable using an autonomous system at all.