The Problem with Agent-Managed Capital
When an AI agent controls a treasury, the attack surface isn't just technical - it's temporal. Agents operate at speeds that make human oversight impossible in real-time. A poorly calibrated position can cascade into liquidation before anyone opens a dashboard.
This isn't hypothetical. As autonomous fund structures proliferate, the gap between "agent made a decision" and "human understood the decision" is measured in blocks, not hours.
Where Traditional Risk Breaks Down
Traditional risk management assumes a human in the loop. Position limits, drawdown rules, concentration thresholds - these all work when someone is watching. But agent-managed treasuries introduce three new failure modes:
- Speed asymmetry: The agent acts faster than governance can respond
- Compound decisions: Sequential trades that individually pass checks but collectively create unacceptable exposure
- Cross-protocol correlation: Positions across multiple DeFi protocols that appear independent but share underlying risk
Building Guardrails That Work
The answer isn't slowing agents down - it's building enforcement at the execution layer. Every transaction must pass through a risk engine that evaluates:
- Position limits: Hard caps per asset, per protocol, per strategy
- Drawdown rules: Maximum acceptable loss over rolling windows
- Correlation checks: Real-time assessment of portfolio-level exposure
- Gas economics: Is this trade worth executing at current gas prices?
The key insight is that these checks must be deterministic and onchain. You can't rely on an off-chain service to veto transactions - the latency budget doesn't allow it, and the trust assumptions break the model.
The Architecture
A practical agent risk system has three layers:
Pre-execution validation: Before any transaction is submitted, the risk engine simulates the post-trade state and validates it against all constraints.
Execution monitoring: During multi-step operations (like unwinding a position across pools), the system monitors intermediate states for constraint violations.
Post-execution reconciliation: After settlement, the system verifies the actual outcome matches the expected outcome within tolerance bounds.
Each layer operates independently. If any layer detects a violation, the system halts and alerts governance.
What's Next
The teams building in this space are converging on a pattern: agent autonomy within deterministic bounds. The agents get faster and smarter, but the guardrails get stricter and more formally verified.
The future isn't agents without constraints - it's agents with constraints so well-designed that they enable more autonomy, not less.