Traditional trustees retire, lose context, change firms. An AI trustee with persistent memory on Walrus only gets sharper — every decision it makes becomes context for the next one. For thirty years. For sixty years.
Agent gets a rule, checks conditions, proposes a distribution. No prior context. Decisions are mechanical — exactly what the rule says, nothing more.
Agent recalls every prior cycle, every beneficiary request, every grantor veto. Before proposing, it asks itself: have I seen this pattern before? Context-aware. Cautious where it should be.
Every cycle the loop closes. Memory makes the agent better.
| Feature | Traditional Trust | ✓ Trustea |
|---|---|---|
| Admin onboarding | $2,000 – $5,000 | ~$2 in gas fees |
| Annual administration | $15k–$30k on $1M AUM | Near $0 |
| Beneficiary requests | Phone calls and letters | Self-service on-chain |
| Audit trail | Filing cabinet | Walrus — encrypted, forever |
| Privacy | Lawyers see everything | Seal — authorized only |
| Compound rules | Lawyer interpretation | AI + AND/OR logic on-chain |
| 30-year cost on $5M | $150k – $450k+ | $15k – $60k |
When a grantor writes a rule, it flows through four layers — each open-source, each auditable on Sui. Nothing is held by us.