1. Introduction
Idle stablecoins are a solved problem in theory and an unsolved one in practice. Depositors want yield without becoming full-time risk managers, and the market punishes the naive strategy: chasing the highest advertised rate concentrates capital into the venue most under stress, which is often paying up precisely because liquidity is fleeing it. When it breaks, the chaser is fully exposed.
Aumo is an agent that manages a shared vault of stablecoins on X Layer and allocates the pooled balance across allowlisted venues to the best risk-adjusted yield, inside limits written into a contract. This paper documents its reasoning architecture and states the safety property that makes an autonomous money-moving agent defensible. Our contributions are: (i) a bounded, explainable risk model with a temporal component learned from the agent's own history; (ii) a scenario-simulation stage that refuses allocations a plausible shock would trap; (iii) a self-reflection loop that recalibrates trust in its signals under a monotonic safety bound; (iv) an adversarial critic as a final gate; and (v) a formal tighten-only guarantee that survives an unreliable or adversarial model.
2. Background: the reasoning taxonomy
Recent surveys organize agentic reasoning into three layers: foundational (planning, tool use, search), self-evolving (memory, reflection, iterative refinement), and collective (multi-agent roles), and distinguish in-context orchestration from post-training (RL/SFT). We use this frame to situate Aumo and to justify a deliberate choice: Aumo is entirely in-context. Its safety is a property of code, not of learned weights.
3. System overview
Each cycle runs five stages, sense, score, simulate, reason, act, and closes with a proof. The planner is deterministic and constructs only moves that already satisfy every on-chain cap, so the contract never rejects a well-formed plan. Formally, let P₀ be the deterministic plan and T any transform applied by a later stage (LLM, stress, critic). Every T in Aumo satisfies
that is, later stages may only raise caution or remove deployments. We call this the tighten-only property. It is what lets an unreliable model participate safely: the worst a bad answer can do is make the vault more conservative or be discarded.
4. The risk model
For each venue v the engine computes five bounded sub-scores in [0,1]: protocol risk (curated base), liquidity risk (venue depth blended with our own exit capacity), peg risk (deviation from par, with a conservative floor for unmonitored real-world-asset venues), utilization risk (lending only), and a correlation-aware concentration risk. They combine as a fixed weighted blend:
Concentration is correlation-aware: exposure to a venue plus everything correlated with it, using a same-kind correlation of 0.75 and a cross-kind correlation of 0.20, so diversifying across uncorrelated kinds genuinely lowers risk while splitting across correlated ones does not. The score maps to a band, low < 0.25 ≤ moderate < 0.50 ≤ elevated < 0.75 ≤ high, and the headline APY is haircut into the quantity the allocator actually ranks on:
5. Temporal awareness
Levels are blind to trajectory. A venue at 70% utilization that has been flat is not the same as one that climbed from 45% in three cycles. Aumo replays its own recorded history and, for each venue, compares the current sample to a rolling window mean, forming an adverse-only momentum score:
where each term is the positive part of the relevant change, normalized by the move that saturates it (a 20-point utilization rise, a 100 bps peg widening, a 30-point drop in the liquidity-to-TVL ratio, a 5% APY spike). Favourable trends contribute nothing, so momentum can only raise risk, and it enters the blend as the additive penalty above with weight 0.15.
6. Scenario simulation
Before committing, Aumo projects the portfolio the plan would create and applies a set of plausible shocks: a 50% collapse in exit liquidity, a 150 bps peg shock to real-world-asset venues, and a lending utilization spike to 95%. A venue is fragile if a shock pushes a held position into the top risk band or makes it exceed all withdrawable liquidity (an un-exitable trap). Fragile venues are denied new deploys, and the fraction of scenarios that breach drives a regime ceiling:
7. Reflection and self-calibration
Aumo grades its own past trend calls. Replaying the history, when momentum flagged a venue, did that venue actually keep deteriorating the next cycle? Let hitRate be the fraction of flags followed by continued deterioration. The agent then scales how much momentum bites:
Because κ ≥ 1 always, reflection is tighten-only by construction: a predictive track record makes the agent more cautious, a poor one leaves it unchanged, and it can never use hindsight to loosen. The reflection is written into the receipt as experience replay.
8. The adversarial critic
After the planner and the model, a distinct critic asks not "is this venue acceptable?" but "how could this specific plan lose money?" It refuses to add to a venue whose momentum exceeds 0.5, refuses a position that would exceed 25% of a venue's exit liquidity, and escalates a doubt that holds the entire cycle if the plan would leave the pool under a 5% idle buffer. It can only remove allocations or hold; de-risking is never blocked. This is the seed of the collective layer: a planner and a skeptic as separate roles.
9. Collective risk steering
Depositors declare an appetite tier on-chain (conservative, moderate, bold). Because a single pooled vault has one allocation, individual per-user risk is impossible; instead the agent share-weights everyone's tier and clamps the result to the owner's hard ceiling:
So depositors can collectively steer the pool safer, or up to but never past, the on-chain hard bound. The preference is a pure signal: it never moves funds or relaxes a cap.
10. Evaluation
We replay a synthetic market of 30 cycles through Aumo's real deterministic engine and, side by side, a naive highest-APY strategy on the identical market. A hot venue looks attractive early (12% APY), then deteriorates over cycles (utilization climbs to 97%, exit liquidity thins, base risk rises) and breaks at cycle 20, haircutting anyone still inside by 35%. The harness is deterministic on purpose: the model only tightens, so proving the core is the honest floor.
| Strategy | Final value | Max drawdown | Exposure at break |
|---|---|---|---|
| Aumo (levels + momentum + stress) | $10,384 | 0.0% | $0 |
| Naive (chase highest APY) | $8,175 | 21.4% | $6,334 |
Aumo captures the early yield, then temporal awareness and rising base risk push the venue past appetite and it exits before the break, ending about 27% ahead with roughly 21 points less drawdown and zero exposure at the break. The naive strategy chases the rate into the wall. The harness is reproducible with a single command.
11. Design rationale: in-context, on purpose
Aumo could be post-trained on historical allocations. We deliberately do not. For an agent that moves real money, deterministic, auditable guardrails beat opaque learned weights: the tighten-only property (Section 3) is a theorem about the code, not a hope about a policy, and every constraint is re-checked by the contract after the model answers. A regulator, a depositor, or an auditor can read exactly why any move was made. That legibility is the product, not an afterthought.
12. Limitations
- Pooled allocation. A single vault has one allocation, so risk steering is collective and share-weighted, not per-depositor. Dissenting depositors can exit but cannot hold a different allocation within the same pool.
- Synthetic evaluation. The backtest uses a constructed market to isolate the deteriorate-then-break failure mode. It demonstrates the mechanism; it is not a claim about realized returns on any live venue.
- Single agent. The critic is one adversarial role. Richer collective reasoning (a peg watcher, a liquidity analyst, a macro-regime agent) is future work.
- Audit status. The contracts are hardened and internally reviewed but have not completed a formal third-party audit. Conservative caps apply.
13. Conclusion
An autonomous treasury agent does not have to choose between yield and trust. By ranking on risk-adjusted yield, reading trajectory rather than level, simulating shocks before it commits, grading its own predictions under a monotonic safety bound, and gating everything behind an adversarial critic and an on-chain contract, Aumo puts stablecoins to work while keeping every move provable. The reasoning is sophisticated; the safety is simple, because it lives in code.
Framework after the survey Agentic Reasoning for Large Language Models (2026). Implementation references: the risk engine, momentum, stress, reflection, and critic modules in the Aumo agent, and the ERC-4626 pool contract. Companion to the Aumo whitepaper and docs. Written by Duke (@dukedotsol).